Endre søk
Begrens søket
1234 1 - 50 of 173
RefereraExporteraLink til resultatlisten
Permanent link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf
Treff pr side
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sortering
  • Standard (Relevans)
  • Forfatter A-Ø
  • Forfatter Ø-A
  • Tittel A-Ø
  • Tittel Ø-A
  • Type publikasjon A-Ø
  • Type publikasjon Ø-A
  • Eldste først
  • Nyeste først
  • Skapad (Eldste først)
  • Skapad (Nyeste først)
  • Senast uppdaterad (Eldste først)
  • Senast uppdaterad (Nyeste først)
  • Disputationsdatum (tidligste først)
  • Disputationsdatum (siste først)
  • Standard (Relevans)
  • Forfatter A-Ø
  • Forfatter Ø-A
  • Tittel A-Ø
  • Tittel Ø-A
  • Type publikasjon A-Ø
  • Type publikasjon Ø-A
  • Eldste først
  • Nyeste først
  • Skapad (Eldste først)
  • Skapad (Nyeste først)
  • Senast uppdaterad (Eldste først)
  • Senast uppdaterad (Nyeste først)
  • Disputationsdatum (tidligste først)
  • Disputationsdatum (siste først)
Merk
Maxantalet träffar du kan exportera från sökgränssnittet är 250. Vid större uttag använd dig av utsökningar.
  • 1.
    Abd-Ellah, Mahmoud Khaled
    et al.
    Electronics and Communications Department, Al-Madina Higher Institute for Engineering and Technology, Giza, Egypt.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system. Faculty of Engineering, Al-Alzhar University, P.O. Box 83513, Qena, Egypt.
    Hamed, Hesham F. A.
    Department of Telecommunications Eng., Egyptian Russian University, Cairo, Egypt. Department of Communications and Electronics, Faculty of Engineering, Minia University, Minia, Egypt.
    Khalaf, Ashraf A. M.
    Department of Communications and Electronics, Faculty of Engineering, Minia University, Minia, Egypt.
    Parallel Deep CNN Structure for Glioma Detection and Classification via Brain MRI Images2019Inngår i: IEEE-ICM 2019 CAIRO-EGYPT: The 31st International Conference on Microelectronics, IEEE, 2019, s. 304-307Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    Although most brain tumor diagnosis studies have focused on tumor segmentation and localization operations, few studies have focused on tumor detection as a time- and effort-saving process. This study introduces a new network structure for accurate glioma tumor detection and classification using two parallel deep convolutional neural networks (PDCNNs). The proposed structure is designed to identify the presence and absence of a brain tumor in MRI images and classify the type of tumor images as high-grade gliomas (HGGs, i.e., glioblastomas) or low-grade gliomas (LGGs). The introduced PDCNNs structure takes advantage of both global and local features extracted from the two parallel stages. The proposed structure is not only accurate but also efficient, as the convolutional layers are more accurate because they learn spatial features, and they are efficient in the testing phase since they reduce the number of weights, which reduces the memory usage and runtime. Simulation experiments were accomplished using an MRI dataset extracted from the BraTS 2017 database. The obtained results show that the proposed parallel network structure outperforms other detection and classification methods in the literature.

  • 2.
    Abd-Ellah, Mahmoud Khaled
    et al.
    Al-Madina Higher Institute for Engineering and Technology, Giza, Egypt .
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system. Faculty of Engineering, Al-Azhar University, Qena, Egypt.
    Khalaf, Ashraf A. M.
    Minia University, Minia, Egypt .
    Hamed, Hesham F. A.
    Egyptian Russian University, Cairo, Egypt. Minia University, Minia, Egypt .
    Deep Convolutional Neural Networks: Foundations and Applications in Medical Imaging2020Inngår i: Deep Learning in Computer Vision: Principles and Applications / [ed] Mahmoud Hassaballah, Ali Ismail Awad, CRC Press, 2020, 1st, s. 233-260Kapittel i bok, del av antologi (Annet vitenskapelig)
  • 3.
    Abd-Ellah, Mahmoud Khaled
    et al.
    Electronics and Communications Department, Al-Madina Higher Institute for Engineering and Technology, Giza, Egypt.
    Khalaf, Ashraf A. M.
    Faculty of Engineering, Minia University, Minia, Egypt.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system. Faculty of Engineering, Al-Azhar University, Qena, Egypt.
    Hamed, Hesham F. A.
    Faculty of Engineering, Minia University, Minia, Egypt.
    TPUAR-Net: Two Parallel U-Net with Asymmetric Residual-Based Deep Convolutional Neural Network for Brain Tumor Segmentation2019Inngår i: Image Analysis and Recognition: 16th International Conference, ICIAR 2019, Waterloo, ON, Canada, August 27–29, 2019, Proceedings, Part II / [ed] Fakhri Karray, Aurélio Campilho, Alfred Yu, Springer, 2019, s. 106-116Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The utilization of different types of brain images has been expanding, which makes manually examining each image a labor-intensive task. This study introduces a brain tumor segmentation method that uses two parallel U-Net with an asymmetric residual-based deep convolutional neural network (TPUAR-Net). The proposed method is customized to segment high and low grade glioblastomas identified from magnetic resonance imaging (MRI) data. Varieties of these tumors can appear anywhere in the brain and may have practically any shape, contrast, or size. Thus, this study used deep learning techniques based on adaptive, high-efficiency neural networks in the proposed model structure. In this paper, several high-performance models based on convolutional neural networks (CNNs) have been examined. The proposed TPUAR-Net capitalizes on different levels of global and local features in the upper and lower paths of the proposed model structure. In addition, the proposed method is configured to use the skip connection between layers and residual units to accelerate the training and testing processes. The TPUAR-Net model provides promising segmentation accuracy using MRI images from the BRATS 2017 database, while its parallelized architecture considerably improves the execution speed. The results obtained in terms of Dice, sensitivity, and specificity metrics demonstrate that TPUAR-Net outperforms other methods and achieves the state-of-the-art performance for brain tumor segmentation.

  • 4.
    Abdunabiev, Isomiddin
    et al.
    Department of Computer and Software, Hanyang University.
    Lee, Choonhwa
    Department of Computer and Software, Hanyang University.
    Hanif, Muhammad
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    An Auto-Scaling Architecture for Container Clusters Using Deep Learning2021Inngår i: 2021년도 대한전자공학회 하계종합학술대회 논문집, DBpia , 2021, s. 1660-1663Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In the past decade, cloud computing has become one of the essential techniques of many business areas, including social media, online shopping, music streaming, and many more. It is difficult for cloud providers to provision their systems in advance due to fluctuating changes in input workload and resultant resource demand. Therefore, there is a need for auto-scaling technology that can dynamically adjust resource allocation of cloud services based on incoming workload. In this paper, we present a predictive auto-scaler for Kubernetes environments to improve the quality of service. Being based on a proactive model, our proposed auto-scaling method serves as a foundation on which to build scalable and resource-efficient cloud systems.

  • 5.
    Adewumi, Tosin
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Södergren, Isabella
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Alkhaled, Lama
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Sabry, Sana Sabah
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Liwicki, Foteini
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Liwicki, Marcus
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Bipol: Multi-axes Evaluation of Bias with Explainability in BenchmarkDatasets2023Inngår i: Proceedings of Recent Advances in Natural Language Processing / [ed] Galia Angelova, Maria Kunilovskaya and Ruslan Mitkov, Incoma Ltd. , 2023, s. 1-10Konferansepaper (Fagfellevurdert)
    Abstract [en]

    We investigate five English NLP benchmark datasets (on the superGLUE leaderboard) and two Swedish datasets for bias, along multiple axes. The datasets are the following: Boolean Question (Boolq), CommitmentBank (CB), Winograd Schema Challenge (WSC), Winogender diagnostic (AXg), Recognising Textual Entailment (RTE), Swedish CB, and SWEDN. Bias can be harmful and it is known to be common in data, which ML models learn from. In order to mitigate bias in data, it is crucial to be able to estimate it objectively. We use bipol, a novel multi-axes bias metric with explainability, to estimate and explain how much bias exists in these datasets. Multilingual, multi-axes bias evaluation is not very common. Hence, we also contribute a new, large Swedish bias-labeled dataset (of 2 million samples), translated from the English version and train the SotA mT5 model on it. In addition, we contribute new multi-axes lexica for bias detection in Swedish. We make the codes, model, and new dataset publicly available.

  • 6.
    Ahmadzadegan, M. Hossein
    et al.
    Azad University, Tehran, Iran.
    Ghorbani, Hamidreza
    Azad University, Tehran, Iran.
    Ståhlbröst, Anna
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Security-Centric Investigation of Social Networks and Preventative Behavioral Analysis of Online Activity by the Kuleshov effect2020Inngår i: 2020 IEE 9th International Conference on Communication Systems and Network Technologies, IEEE, 2020, s. 271-276Konferansepaper (Fagfellevurdert)
    Abstract [en]

    The Internet beside providing many advantages has some disadvantages. The full unfamiliarity with cyberspace can bring harms and inconvenience to families. This study attempted to introduce the disadvantages and threats on the Internet and present some countermeasures to decrease such threats and harms. Social networks are currently so epidemic that even many companies and organizations formally supply the internal communication system among their members and employees through this social networks and the number of the users of such networks is increasing daily. The term “information security” covers a broad spectrum of activities in an organization. Information security means preventing the unauthorized access to information and products or apply some changes or delete such information using a series of processes. Such actions can be considered as a protection package against current resources in different situations (e.g., a hacker attacks which occurs a lot) by those in charge of information security. One of the most important issues which is considered as a security challenge in these networks is the security and retention of the data and information shared by users from unauthorized access and unwanted disclosure. In this article the Kuleshov effect from cinema has been deployed to explain and launch a behavioral analysis of online activities.

  • 7.
    Alani, Mohammed M.
    et al.
    Seneca College, Toronto, Canada.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system. College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 17551, United Arab Emirates; Faculty of Engineering, Al-Azhar University, Qena P.O. Box 83513, Egypt; Centre for Security, Communications and Network Research, University of Plymouth, Plymouth PL4 8AA, U.K..
    AdStop: Efficient Flow-based Mobile Adware Detection using Machine Learning2022Inngår i: Computers & security (Print), ISSN 0167-4048, E-ISSN 1872-6208, Vol. 117, artikkel-id 102718Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In recent years, mobile devices have become commonly used not only for voice communications but also to play a major role in our daily activities. Accordingly, the number of mobile users and the number of mobile applications (apps) have increased exponentially. With a wide user base exceeding 2 billion users, Android is the most popular operating system worldwide, which makes it a frequent target for malicious actors. Adware is a form of malware that downloads and displays unwanted advertisements, which are often offensive and always unsolicited. This paper presents a machine learning-based system (AdStop) that detects Android adware by examining the features in the flow of network traffic. The design goals of AdStop are high accuracy, high speed, and good generalizability beyond the training dataset. A feature reduction stage was implemented to increase the accuracy of Adware detection and reduce the time overhead. The number of relevant features used in training was reduced from 79 to 13 to improve the efficiency and simplify the deployment of AdStop. In experiments, the tool had an accuracy of 98.02% with a false positive rate of 2% and a false negative rate of 1.9%. The time overhead was 5.54 s for training and 9.36 µs for a single instance in the testing phase. In tests, AdStop outperformed other methods described in the literature. It is an accurate and lightweight tool for detecting mobile adware.

  • 8.
    Alani, Mohammed M.
    et al.
    School of IT Administration and Security, Seneca College of Applied Arts and Technology, Toronto Metropolitan University, Toronto, ON, Canada.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system. College of Information Technology, United Arab Emirates University, Al Ain, UAE; Electrical Engineering Department, Faculty of Engineering, Al-Azhar University, Qena, Egypt; Centre for Security, Communications and Network Research, University of Plymouth, United Kingdom.
    An Intelligent Two-Layer Intrusion Detection System for the Internet of Things2023Inngår i: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 19, nr 1, s. 683-692Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    The Internet of Things (IoT) has become an enabler paradigm for different applications, such as healthcare, education, agriculture, smart homes, and recently, enterprise systems (E-IoTs). Significant advances in IoT networks have been hindered by security vulnerabilities and threats, which, if not addressed, can negatively impact the deployment and operation of IoT-enabled systems. This study addresses IoT security and presents an intelligent two-layer intrusion detection system for IoT. The system's intelligence is driven by machine learning techniques for intrusion detection, with the two-layer architecture handling flow-based and packet-based features. By selecting significant features, the time overhead is minimized without affecting detection accuracy. The uniqueness and novelty of the proposed system emerge from combining machine learning and selection modules for flow-based and packet-based features. The proposed intrusion detection works at the network layer, and hence, it is device and application transparent. In our experiments, the proposed system had an accuracy of 99.15% for packet-based features with a testing time of 0.357 μs. The flow-based classifier had an accuracy of 99.66% with a testing time of 0.410 μs. A comparison demonstrated that the proposed system outperformed other methods described in the literature. Thus, it is an accurate and lightweight tool for detecting intrusions in IoT systems.

  • 9.
    Alani, Mohammed M.
    et al.
    Department of Computer Science, Toronto Metropolitan University, Toronto, ON, Canada; School of IT Administration and Security, Seneca College of Applied Arts and Technology, Toronto, ON M2J 2X5, Canada.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system. College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates; Electrical Engineering Department, Faculty of Engineering, Al-Azhar University, Qena 83513, Egypt; Centre for Security, Communications and Network Research, University of Plymouth, Plymouth PL4 8AA, U.K..
    PAIRED: An Explainable Lightweight Android Malware Detection System2022Inngår i: IEEE Access, E-ISSN 2169-3536, Vol. 10, s. 73214-73228Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    With approximately 2 billion active devices, the Android operating system tops all other operating systems in terms of the number of devices using it. Android has gained wide popularity not only as a smartphone operating system, but also as an operating system for vehicles, tablets, smart appliances, and Internet of Things devices. Consequently, security challenges have arisen with the rapid adoption of the Android operating system. Thousands of malicious applications have been created and are being downloaded by unsuspecting users. This paper presents a lightweight Android malware detection system based on explainable machine learning. The proposed system uses the features extracted from applications to identify malicious and benign malware. The proposed system is tested, showing an accuracy exceeding 98% while maintaining its small footprint on the device. In addition, the classifier model is explained using Shapley Additive Explanation (SHAP) values.

  • 10.
    Alawadi, Sadi
    et al.
    Department of Information Technology, Uppsala University, Uppsala, Sweden.
    Kebande, Victor R.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Dong, Yuji
    School of Internet of Things, Xi’an Jiaotong-Liverpool University, Suzhou, China.
    Bugeja, Joseph
    Department of Computer Science, Malmö University, Malmö, Sweden.
    Persson, Jan A.
    Department of Computer Science, Malmö University, Malmö, Sweden.
    Olsson, Carl Magnus
    Department of Computer Science, Malmö University, Malmö, Sweden.
    A Federated Interactive Learning IoT-Based Health Monitoring Platform2021Inngår i: New Trends in Database and Information Systems: ADBIS 2021 Short Papers, Doctoral Consortium and Workshops: DOING, SIMPDA, MADEISD, MegaData, CAoNS, Tartu, Estonia, August 24-26, 2021, Proceedings / [ed] Ladjel Bellatreche; Marlon Dumas; Panagiotis Karras; Raimundas Matulevičius; Ahmed Awad; Matthias Weidlich; Mirjana Ivanović; Olaf Hartig, Springer, 2021, s. 235-246Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Remote health monitoring is a trend for better health management which necessitates the need for secure monitoring and privacy-preservation of patient data. Moreover, accurate and continuous monitoring of personal health status may require expert validation in an active learning strategy. As a result, this paper proposes a Federated Interactive Learning IoT-based Health Monitoring Platform (FIL-IoT-HMP) which incorporates multi-expert feedback as ‘Human-in-the-loop’ in an active learning strategy in order to improve the clients’ Machine Learning (ML) models. The authors have proposed an architecture and conducted an experiment as a proof of concept. Federated learning approach has been preferred in this context given that it strengthens privacy by allowing the global model to be trained while sensitive data is retained at the local edge nodes. Also, each model’s accuracy is improved while privacy and security of data has been upheld. 

    Fulltekst (pdf)
    fulltext
  • 11.
    Al-Azzawi, Sana Sabah Sabry
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Kovács, György
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Mokayed, Hamam
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Chronéer, Diana
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Liwicki, Foteini
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Liwicki, Marcus
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Innovative Education Approach Toward Active Distance Education: a Case Study in the Introduction to AI course2022Inngår i: Conference Proceedings. The Future of Education 2022, 2022Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In this paper, we first describe various synchronous and asynchronous methods for enhancing student engagement in big online courses. We showcase the implementation of these methods in the “Introduction to Artificial Intelligence (AI)” course at Luleå University of Technology, which has attracted around 500 students in each of its iterations (twice yearly, since 2019). We also show that these methods can be applied efficiently, in terms of the teaching hours required. With the increase in digitization and student mobility, the demand for improved and personalized content delivery for distance education has also increased. This applies not only in the context of traditional undergraduate education, but also in the context of adult education and lifelong learning. This higher level of demand, however, introduces a challenge, especially as it is typically combined with a shortage of staff and needs for efficient education. This challenge is further amplified by the current pandemic situation, which led to an even bigger risk of student-dropout. To mitigate this risk, as well as to meet the increased demand, we applied various methods for creating engaging interaction in our pedagogy based on Moor’s framework: learner-to-learner, learner-to-instructor, and learner-to-content engagement strategies. The main methods of this pedagogy are as follows: short, and interactive videos, active discussions in topic-based forums, regular live sessions with group discussions, and the introduction of optional content at many points in the course, to address different target groups. In this paper, we show how we originally designed and continuously improved the course, without requiring more than 500 teaching hours per iteration (one hour per enrolled student), while we also managed to increase the successful completion rate of the participants by 10%, and improved student engagement and feedback for the course by 50%. We intend to share a set of best-practices applicable to many other e-learning courses in ICT.

    Fulltekst (pdf)
    fulltext
  • 12.
    Al-Dhaqm, Arafat
    et al.
    School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Skudai 81310, Malaysia; Department of Computer Science, Aden Community College, Aden 999101, Yemen.
    Ikuesan, Richard A.
    Department of Cybersecurity and Networking, School of Information Technology, Community College Qatar, Doha 00974, Qatar.
    Kebande, Victor R.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Shukor, Razak
    School of Computing, Faculty of Engineering, Universiti Teknologi Malaysia (UTM), Skudai 81310, Malaysia.
    Ghabban, Fahad M.
    Information System Department, College of Computer Science and Engineering, Taibah University, Madina 42353, Saudi Arabia.
    Research Challenges and Opportunities in Drone Forensics Models2021Inngår i: Electronics, E-ISSN 2079-9292, Vol. 10, nr 13, artikkel-id 1519Artikkel, forskningsoversikt (Fagfellevurdert)
    Abstract [en]

    The emergence of unmanned aerial vehicles (also referred to as drones) has transformed the digital landscape of surveillance and supply chain logistics, especially in terrains where such was previously deemed unattainable. Moreover, the adoption of drones has further led to the proliferation of diverse drone types and drone-related criminality, which has introduced a myriad of security and forensics-related concerns. As a step towards understanding the state-of-the-art research into these challenges and potential approaches to mitigation, this study provides a detailed review of existing digital forensic models using the Design Science Research method. The outcome of this study generated in-depth knowledge of the research challenges and opportunities through which an effective investigation can be carried out on drone-related incidents. Furthermore, a potential generic investigation model has been proposed. The findings presented in this study are essentially relevant to forensic researchers and practitioners towards a guided methodology for drone-related event investigation. Ultimately, it is important to mention that this study presents a background for the development of international standardization for drone forensics.

    Fulltekst (pdf)
    fulltext
  • 13.
    Al-Dhaqm, Arafat
    et al.
    Faculty of Engineering, School of Computing, Computer Science Department, Universiti Teknologi Malaysia (UTM), Skudai 81310, Johor, Malaysia. Computer Science Department, Aden Community College, Aden 999101, Yemen.
    Razak, Shukor
    Faculty of Engineering, School of Computing, Computer Science Department, Universiti Teknologi Malaysia (UTM), Skudai 81310, Johor, Malaysia.
    Ikuesan, Richard A.
    Department of Cybersecurity and Networking, School of Information Technology, Community College Qatar, Doha 7344, Qatar.
    Kebande, Victor R.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system. Department of Computer Science and Media Technology Department, Malmö Universitet, Nordenskiöldsgatan 1, 21119 Malmö, Sweden.
    Othman, Siti H.
    Faculty of Engineering, School of Computing, Computer Science Department, Universiti Teknologi Malaysia (UTM), Skudai 81310, Johor, Malaysia.
    Face validation of database forensic investigation metamodel2021Inngår i: Infrastructures, E-ISSN 2412-3811, Vol. 6, nr 2, s. 1-19Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Using a face validity approach, this paper provides a validation of the Database Forensic Investigation Metamodel (DBFIM). The DBFIM was developed to solve interoperability, heterogeneity, complexity, and ambiguity in the database forensic investigation (DBFI) field, where several models were identified, collected, and reviewed to develop DBFIM. However, the developed DBFIM lacked the face validity-based approach that could ensure DBFIM’s applicability in the DBFI field. The completeness, usefulness, and logic of the developed DBFIM needed to be validated by experts. Therefore, the objective of this paper is to perform the validation of the developed DBFIM using the qualitative face validity approach. The face validity method is a common way of validating metamodels through subject expert inquiry on the domain application of the metamodel to assess whether the metamodel is reasonable and compatible based on the outcomes. For this purpose, six experts were nominated and selected to validate the developed DBFIM. From the expert review, the developed DBFIM was found to be complete, coherent, logical, scalable, interoperable, and useful for the DBFI field.

  • 14.
    Amin, Marian H.
    et al.
    Faculty of Management Technology, German University in Cairo, Cairo, Egypt.
    Mohamed, Ehab K.A
    Faculty of Management Technology, German University in Cairo, Cairo, Egypt.
    Elragal, Ahmed
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Corporate Disclosure via Social Media: A Data Science Approach2020Inngår i: Online information review (Print), ISSN 1468-4527, E-ISSN 1468-4535, Vol. 44, nr 1, s. 278-298Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Purpose - The aim of this paper is to investigate corporate financial disclosure via Twitter among the top listed 350 companies in the UK as well as identify the determinants of the extent of social media usage to disclose financial information.

    Design/methodology/approach – This study applies an unsupervised machine learning technique, namely, Latent Dirichlet Allocation (LDA) topic modeling to identify financial disclosure tweets. Panel, Logistic, and Generalized Linear Model Regressions are also run to identify the determinants of financial disclosure on Twitter focusing mainly on board characteristics.

    Findings – Topic modeling results reveal that companies mainly tweet about 12 topics, including financial disclosure, which has a probability of occurrence of about 7 percent. Several board characteristics are found to be associated with the extent of Twitter usage as a financial disclosure platform, among which are board independence, gender diversity, and board tenure.

    Originality/value – Extensive literature examines disclosure via traditional media and its determinants, yet this paper extends the literature by investigating the relatively new disclosure channel of social media. This study is among the first to utilize machine learning, instead of manual coding techniques, to automatically unveil the tweets’ topics and reveal financial disclosure tweets. It is also among the first to investigate the relationships between several board characteristics and financial disclosure on Twitter; providing a distinction between the roles of executive versus non-executive directors relating to disclosure decisions.

  • 15.
    Andersson, Karl
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap. Luleå tekniska universitet, Institutionen för system- och rymdteknik, CDT. Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Tan, Hwee-Pink
    Singapore Management University, Singapore.
    LCN 2019 Message from the TPC Chairs2019Inngår i: 2019 IEEE 44th Conference on Local Computer Networks (LCN) / [ed] Karl Andersson, Hwee-Pink Tan, Sharief Oteafy, IEEE, 2019, s. i-iKonferansepaper (Annet vitenskapelig)
  • 16.
    Andersson, Simon
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Problems in information classification: insights from practice2023Inngår i: Information and Computer Security, E-ISSN 2056-4961, Vol. 31, nr 4, s. 449-462Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Purpose

    This study aims to identify problems connected to information classification in theory and to put those problems into the context of experiences from practice.

    Design/methodology/approach

    Five themes describing problems are discussed in an empirical study, having informants represented from both a public and a private sector organization.

    Findings

    The reasons for problems to occur in information classification are exemplified by the informants’ experiences. The study concludes with directions for future research.

    Originality/value

    Information classification sustains the basics of security measures. The human–organizational challenges are evident in the activities but have received little attention in research.

    Fulltekst (pdf)
    fulltext
  • 17.
    Ashaju, Oluwafemi
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Investigating the Effects of Information Security Awareness in the Third Sector2020Independent thesis Advanced level (degree of Master (Two Years)), 80 poäng / 120 hpOppgave
    Abstract [en]

    Information security awareness (ISA) focuses on the user’s responsibility and understanding of risk, to ensure that acceptable working practices are adopted under these broad principles, thereby reducing the likelihood of legal, financial and reputational risk related to the organization and individual. However, the third sector organization is behind in the security awareness maturity level. This research aims to understand and evaluate the level of information security awareness (ISA) knowledge in third sector organizations. The study was conducted with mixed-method design, combining the qualitative and quantitative approaches. A semi-structured interview method was used to gather data, transcribe it, and analyse it with a thematic framework analysis. The quantitative approach uses a questionnaire survey method was used to investigate the knowledge of information security awareness. The main findings present a lack of security awareness in the third sector and gaps in good security behaviour. 

     

  • 18.
    Awad, Ali Ismail
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system. United Arab Emirates University, Al Ain, United Arab Emirates; Al-Azhar University, Qena, Egypt.
    Abawajy, Jemal
    Deakin University, Australia.
    Preface2022Inngår i: Security and Privacy in the Internet of Things: Architectures, Techniques, and Applications / [ed] Ali Ismail Awad; Jemal Abawajy, John Wiley & Sons, 2022, s. xix-xxiiiKapittel i bok, del av antologi (Annet vitenskapelig)
  • 19.
    Awad, Ali Ismail
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system. United Arab Emirates University, Al Ain, UAE; Al-Azhar University, Qena, Egypt.
    Abawajy, JemalDeakin University, Australia.
    Security and Privacy in the Internet of Things: Architectures, Techniques, and Applications2022Collection/Antologi (Annet vitenskapelig)
    Abstract [en]

    The vast amount of data generated by the Internet of Things (IoT) has made information and cyber security vital for not only personal privacy, but also for the sustainability of the IoT itself. Security and Privacy in the Internet of Things brings together high-quality research on IoT security models, architectures, techniques, and application domains. This concise yet comprehensive volume explores state-of-the-art mitigations in IoT security while addressing important security and privacy challenges across different IoT layers. The book provides timely coverage of IoT architecture, security technologies and mechanisms, and applications. The authors outline emerging trends in IoT security and privacy with a focus on areas such as smart environments and e-health. Topics include authentication and access control, attack detection and prevention, securing IoT through traffic modeling, human aspects in IoT security, and IoT hardware security. Presenting the current body of knowledge in a single volume, Security and Privacy in the Internet of Things:

    • Discusses a broad range of IoT attacks and defense mechanisms • Examines IoT security and privacy protocols and approaches • Covers both the logical and physical security of IoT devices • Addresses IoT security through network traffic modeling • Describes privacy preserving techniques in smart cities • Explores current threat and vulnerability analyses

    Security and Privacy in the Internet of Things: Architectures, Techniques, and Applications is essential reading for researchers, industry practitioners, and students involved in IoT security development and IoT systems deployment.

  • 20.
    Awad, Ali Ismail
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system. Faculty of Engineering, Al-Azhar University, Qena, Egypt.
    Furnell, Steven
    School of Computer Science, University of Nottingham, Nottingham, UK.
    Paprzycki, Marcin
    Systems Research Institute, Polish Academy of Sciences, Warsaw, Poland.
    Sharma, Sudhir Kumar
    Institute of Information Technology and Management, New Delhi, India.
    Preface2021Inngår i: Security in Cyber-Physical Systems: Foundations and Applications / [ed] Ali Ismail Awad; Steven Furnell; Marcin Paprzycki; Sudhir Kumar Sharma, Springer Nature, 2021, Vol. 339, s. v-ixKapittel i bok, del av antologi (Annet vitenskapelig)
  • 21.
    Baker, Thar
    et al.
    Department of Computer Science, College of Computing and Informatics, University of Sharjah, United Arab Emirates.
    Guo, Zehua
    Beijing Institute of Technology, China.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system. Electrical Engineering Department, Faculty of Engineering, Al-Azhar University at Qena, Qena 83513, Egypt. Centre for Security, Communications and Network Research, University of Plymouth, Plymouth PL4 8AA, United Kingdom.
    Wang, Shangguang
    State Key Laboratory of Networking and Switching Technology, Beijing University of Posts and Telecommunications, China.
    Fung, Benjamin C.M.
    School of Information Studies, McGill University, Montreal, Canada.
    Enabling Technologies for Energy Cloud2021Inngår i: Journal of Parallel and Distributed Computing, ISSN 0743-7315, E-ISSN 1096-0848, Vol. 152, s. 108-110Artikkel i tidsskrift (Annet vitenskapelig)
    Abstract [en]

    We are thrilled and delighted to present this special issue, which emphasises on the novel area of Enabling Technologies for Energy Cloud. This guest editorial provides an overview of all articles accepted for publication in this special issue.

  • 22.
    Bakumenko, Alexander
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Elragal, Ahmed
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Detecting Anomalies in Financial Data using Machine Learning Algorithms2022Inngår i: Systems, E-ISSN 2079-8954, Vol. 10, nr 5, artikkel-id 130Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Bookkeeping data free of fraud and errors is a cornerstone of legitimate business operations. Highly complex and laborious financial auditors’ work calls for finding new solutions and algorithms to ensure the correctness of financial statements. Both supervised and unsupervised machine learning (ML) techniques, nowadays, are being successfully applied to detect fraud and anomalies in data. In accounting, it is a long-established problem to detect financial misstatements deemed anomalies in General Ledger (GL) data. Currently, widely used techniques such as random sampling and manual assessment of bookkeeping rules become challenging and unreliable due to increasing data volumes and unknown fraudulent patterns. To address the sampling risk and financial audit inefficiency, we applied seven supervised ML techniques inclusive of Deep Learning and two unsupervised ML techniques such as Isolation Forest and Autoencoders. We trained and evaluated our models on a real-life GL dataset and used data vectorization to resolve journal entry size variability. The evaluation results showed that the best trained supervised and unsupervised models have high potential in detecting predefined anomaly types as well as in efficiently sampling data to discern higher-risk journal entries. Based on our findings, we discussed possible practical implications of the resulting solutions in the accounting context.

    Fulltekst (pdf)
    fulltext
  • 23.
    Basuony, Mohamed A.K.
    et al.
    Department of Accounting, School of Business, American University in Cairo, Cairo, Egypt.
    Mohamed, Ehab K.A.
    Department of Accounting and Finance, Faculty of Management Technology, German University in Cairo, Cairo, Egypt.
    Elragal, Ahmed
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Hussainey, Khaled
    Department of Accounting and Financial Management, Faculty of Business and Law, Portsmouth Business School, University of Portsmouth, Portsmouth, UK.
    Big data analytics of corporate internet disclosures2022Inngår i: Accounting Research Journal, ISSN 1030-9616, E-ISSN 1839-5465, Vol. 35, nr 1, s. 4-20Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Purpose: This study aims to investigate the extent and characteristics of corporate internet disclosure via companies’ websites as well via social media and networks sites in the four leading English-speaking stock markets, namely, Australia, Canada, the UK and the USA.

    Design/methodology/approach: A disclosure index comprising a set of items that encompasses two facets of online disclosure, namely, company websites and social media sites, is used. This paper adopts a data science approach to investigate corporate internet disclosure practices among top listed firms in Australia, Canada, the UK and the USA.

    Findings: The results reveal the underlying relations between the determining factors of corporate disclosure, i.e. profitability, leverage, liquidity and firm size. Profitability in its own has no great effect on the degree of corporate internet disclosure whether via company websites or social media sites. Liquidity has an impact on the degree of disclosure. Firm size and leverage appear to be the most important factors driving better disclosure via social media. American companies tend to be on the cutting edge of technology when it comes to corporate disclosure.

    Practical implications: This paper provides new insights into corporate internet disclosure that will benefit all stakeholders with an interest in corporate reporting. Social media is an influential means of communication that can enable corporate office to get instant feedback enhancing their decision-making process.

    Originality/value: To the best of the authors’ knowledge, this study is amongst few studies of corporate disclosure via social media platforms. This study has adopted disclosure index incorporating social media as well as applying data science approach in disclosure in an attempt to unfold how accounting could benefit from data science techniques.

  • 24.
    Bergsten, Daniela
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Understanding the Modus Operandi of Advanced Persistent Threats: A comparison of the Modus Operandi of Advanced Persistent Threats and their Impact2020Independent thesis Advanced level (degree of Master (Two Years)), 80 poäng / 120 hpOppgave
    Abstract [en]

    Since Advanced Persistent Threats (APTs) are the most sophisticated form of cyber weapon to date, previous research has indicated that further knowledge about the actors and their Modus Operandi (MO) is needed as the groups are highly organized, skilled and motivated when engaging in cyberoperations with different aims. This thesis poses the research question: how does the desired impact of an APT affect its MO? To answer the research question, a cross-case study is performed using a qualitative case study design. The method of structured focused comparison is employed where the cases of the Russian attributed APT the Sandworm Team and the North Korean-linked APT the Lazarus Group which have engaged in numerous cyberoperations with multiple impacts are compared against the Russian attributed APT Turla and the North Korean attributed APT Kimsuky which have performed numerous cyberoperations with a single impact. The findings, using the MITRE ATT&CK framework, show that there are similarities across the cases in terms of the techniques used but differences in terms of malwares used. The findings therefore indicate that the malware may be the key determinant of the impact of a cyberoperation by an APT.

  • 25.
    Bergström, Erik
    et al.
    Jönköping University.
    Lundgren, Martin
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Stress Amongst Novice Information Security Risk Management Practitioners2019Inngår i: International Journal on Cyber Situational Awareness, ISSN 2057-2182, Vol. 4, nr 1, s. 128-154Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Today, information is a key asset for many organisations. Reducing risks of information compromise is increasingly prioritised. However, there is an incomplete understanding of how organisations with limited security knowledge and experience manage information security risks in practice. Previous studies have suggested that security-novice employees faced with burdensome, complex, and ambiguous security requirements can experience security-related stress (SRS), and ultimately influence their security decisions. In this study, we further this research stream by suggesting that SRS can similarly be found with security-novice managers responsible for developing and practising information security risk management (ISRM). Two organisations were targeted in the study using a case study approach, to obtain data about their practices, using SRS as an analytical lens. The study found various examples where SRS influenced security-novice managers’ decisions, and identifies several stressors and stress inhibitors in the ISRM process and supporting ISRM tools, and discusses the implications for practitioners.

    Fulltekst (pdf)
    fulltext
  • 26.
    Bergström, Erik
    et al.
    School of Informatics, University of Skövde, Skövde.
    Lundgren, Martin
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Ericson, Åsa
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Revisiting information security risk management challenges: a practice perspective2019Inngår i: Information and Computer Security, ISSN 2056-4961, Vol. 27, nr 3, s. 358-372Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Purpose – The study aims to revisit six previously defined challenges in information security risk management to provide insights into new challenges based on current practices.

    Design/methodology/approach – The study is based on an empirical study consisting of in-depth interviews with representatives from public sector organisations. The data were analysed by applying a practice-based view, i.e. the lens of knowing (or knowings). The results were validated by an expert panel.

    Findings – Managerial and organisational concerns that go beyond a technical perspective have been . found, which affect the ongoing social build-up of knowledge in everyday information security work.

    Research limitations/implications – The study has delimitation as it consists of data from four public sector organisations, i.e. statistical analyses have not been in focus, while implying a better understanding of what and why certain actions are practised in their security work.

    Practical implications – The new challenges that have been identified offer a refined set of actionable advice to practitioners, which, for example, can support cost-efficient decisions and avoid unnecessary security trade-offs.

    Originality/value – Information security is increasingly relevant for organisations, yet little is still known about how related risks are handled in practice. Recent studies have indicated a gap between the espoused and the actual actions. Insights from actual, situated enactment of practice can advise on process adaption and suggest more fit approaches.

  • 27.
    Bernsmed, Karin
    et al.
    SINTEF Digital, Trondheim, Norway.
    Bour, Guillaume
    SINTEF Digital, Trondheim, Norway.
    Lundgren, Martin
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Bergström, Erik
    Jönköping University, Jönköping, Sweden.
    An evaluation of practitioners’ perceptions of a security risk assessment methodology in air traffic management projects2022Inngår i: Journal of Air Transport Management, ISSN 0969-6997, E-ISSN 1873-2089, Vol. 102, artikkel-id 102223Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Cyber security is a key enabler for safe Air Traffic Management (ATM). This paper presents results from an empirical study, in which we have investigated and evaluated the use of the Security Risk Assessment Methodology for SESAR (SecRAM) in European ATM research and development projects. The study was performed with the intention to find and document common issues and aspects that could be improved in the methodology. The results from the study reveal that while most of the practitioners had a positive perception of the methodology itself, they were less satisfied with the process of applying it in their projects. Based on the results, we provide a number of recommendations, which aim to improve the security risk assessment process in the ATM domain.

    Fulltekst (pdf)
    fulltext
  • 28.
    Bomström, Henri
    et al.
    M3S Research Unit, University of Oulu, Pentti Kaiteran Katu 1, 90014 Oulu, Finland.
    Annanperä, Elina
    M3S Research Unit, University of Oulu, Pentti Kaiteran Katu 1, 90014 Oulu, Finland.
    Kelanti, Markus
    M3S Research Unit, University of Oulu, Pentti Kaiteran Katu 1, 90014 Oulu, Finland.
    Xu, Yueqiang
    M3S Research Unit, University of Oulu, Pentti Kaiteran Katu 1, 90014 Oulu, Finland.
    Mäkelä, Satu-Marja
    VTT Technical Research Centre of Finland Ltd, Kaitoväylä 1, 90571 Oulu, Finland.
    Immonen, Milla
    VTT Technical Research Centre of Finland Ltd, Kaitoväylä 1, 90571 Oulu, Finland.
    Siirtola, Pekka
    BISG Research Group, University of Oulu, Pentti Kaiteran Katu 1, 90014 Oulu, Finland.
    Teern, Anna
    M3S Research Unit, University of Oulu, Pentti Kaiteran Katu 1, 90014 Oulu, Finland.
    Liukkunen, Kari
    M3S Research Unit, University of Oulu, Pentti Kaiteran Katu 1, 90014 Oulu, Finland.
    Päivärinta, Tero
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system. M3S Research Unit, University of Oulu, Pentti Kaiteran Katu 1, 90014 Oulu, Finland;;Luleå University of Technology, Digital Services and Systems, Porsön, 971 87, Luleå, Sweden.
    Digital Twins About Humans—Design Objectives From Three Projects2022Inngår i: Journal of Computing and Information Science in Engineering, ISSN 1530-9827, E-ISSN 1944-7078, Vol. 22, nr 5, artikkel-id 050907Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Digital twin (DT) emerges as a key concept of the Industry 4.0 paradigm and beyond. However, the current literature lacks focus on humans and human activities as a part of complex system DTs. Acknowledging human aspects in DTs can enhance work performance, well-being, motivation, and personal development of professionals. This study examines emerging requirements for human digital twins (HDTs) in three use cases of industry–academia collaboration on complex systems. The results draw together the overall design problem and four design objectives for HDTs. We propose to combine the machine and human-related aspects of DTs and highlight the need for virtual-to-virtual interoperability between HDTs and machines alike. Furthermore, we outline differences between humans and machines regarding digital twinning by addressing human activities and knowledge-based behavior on systems. Design of HDTs requires understanding of individual professional characteristics, such as skills and information preferences, together with twinning between the physical and digital machine entities and interactions between the human and machine DTs. As the field moves toward including humans as a part of the DT concept, incorporating HDTs in complex systems emerges as an increasingly significant issue.

  • 29.
    Booth, Todd
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Design Principles for Network Distributed Denial of Service Defense2022Doktoravhandling, med artikler (Annet vitenskapelig)
    Abstract [en]

    People, organizations and society are become more and more dependent upon access to Information Systems. Most Information Systems are accessible via the Internet. It is becoming easier and easier to perform successful network attacks against these Information Systems, which causes the system to become unavailable for its intended users. It is also very inexpensive to launch a successful network DRDoS attack against an organization’s servers. One type of distributed denial of service (DDoS) network attack sends a very large volume of traffic towards the victim’s servers. The most common of these volumetric DDoS attacks are described as reflective DDoS service (DRDoS) attacks and the DRDoS defense is the main contribution of this thesis. For years, you have been able to even rent network attack services from criminal organizations, which are often in the form of DRDoS network attacks.

    The Design Science Research (DSR) approach was used for my research. Included are the DSR cycles performed, including the artifact evaluations. The relationship between the DSR cycles and the published research papers is presented in the paper summary section. The first two papers formed the DSR problem definition. The next three papers used a variety of information hiding techniques to mitigate network attacks. The last paper proposed a different design principle, based on filtering traffic before it reached the public cloud providers. This proposed DRDoS defense approach is to have the public cloud provider request their IP neighbors to filter or drop certain traffic for a big IP block of IP addresses. Then the provider gives IP addresses to their customers, who want this protection, from the big IP block. This way the provider can provide DRDoS protection for hundreds of thousands of customers, with a few firewall rules and the filtering of malicious traffic occurs at the network edge. This solution prevents most of the DRDoS attack traffic from even reaching the public cloud provider. This last research is focused on protecting servers from DRDoS attacks, where the servers are accessible via the Internet and where the servers are or can be hosted via a public cloud provider. This public cloud provider hosting includes accessibility via cloud offerings, such as with Amazon’s Web Services (AWS), Google’s Compute Cloud (GCP), and Microsoft’s Azure. To simplify the discussion, this thesis will focus on Web servers, as the example.

    The research has been generalized into the following two research design principal contributions. My thesis, including the design principles, contributes to the state of the art network DDoS defense in the following ways:

    1. Divide and Search for Malicious Network Traffic. After the attack is detected, the IP, Web, and/or DNS address information is changed  This mitigates the attacks since the attacker will not be able to quickly learn the new DNS, Web, or IP connectivity information. This has the effect to reduce or mitigate the effect of the DDoS attacks.

    2.  Ask IPX Neighbors to Pre-process Network Traffic. With this design principle, we have two types of features. One feature is to stop malicious traffic. This mitigates the attacks at the public cloud provider’s neighbors, so that most of the malicious traffic never even arrives to the cloud provider. This way, the cloud provider no longer needs to process the malicious traffic to filter it out. The other feature is to provide a different quality of service (QoS) for incoming traffic. This allows the public cloud provider’s neighbor to treat the traffic as higher or lower priority traffic.

    In this thesis, the contributions are how to improve the state of the art DDoS defense solutions, concerning network attacks against Internet accessible servers. We believe that our DRDoS defense contribution is better, more efficient, and/or more effective than the current state of the art DDRoS solutions. Our contributions are focused on network layer attacks as opposed to application, presentation, or transport layer attacks.

    Fulltekst (pdf)
    fulltext
  • 30.
    Booth, Todd
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Andersson, Karl
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Mitigating DRDoS Network Attacks via Consolidated Deny Filter Rules2020Inngår i: Research Briefs on Information & Communication Technology Evolution (ReBICTE), E-ISSN 2383-9201, Vol. 6, artikkel-id 2Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This article is concerning distributed reflection denial of service (DRDoS) attacks.  These DRDoS attacks are more frequent and large scale, and are one of the biggest threats on the Internet. This paper discusses the best way to defend from these attacks using public cloud defenses, such as Amazon AWS, Google GCP, and Microsoft Azure, at a very low cost.  Our mitigation strategy takes advantage of the fact that the attacker does not have full control to change the source IP port to anything they want, when used in these reflective attacks.  We propose to have the customer host their Web servers and other types of supporting servers in the public cloud.  The cloud provider then reserves a /CIDR block of IP addresses, which will be protected.  The cloud providers customers who opt in, will be allocated an IP address from this block.  This block will be used as the source IP address deny portion of the firewall rule-sets.  Then the public cloud providers will use BGP4 Flow-Spec or some scripting solution, to have their IP service provider neighbors perform the actual filtering of the DRDoS attack traffic concerning attacks against these servers.

    Fulltekst (pdf)
    Full Paper
  • 31.
    Brännvall, Rickard
    et al.
    Research Institutes of Sweden.
    Kovács, György
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, EISLAB.
    Rizk, Aya
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Lehtonen, Viktor
    Eriksson, Ann-Christin
    Edman, Tobias
    Liwicki, Marcus
    National Space Data Lab on Kubernetes2019Konferansepaper (Annet vitenskapelig)
    Abstract [en]

    The National Space Data Lab is a collaboration project between Swedish National Space Agency, RISE Research Institutes of Sweden, Luleå University of Technology and AI Sweden. It will be a national knowledge and data hub for Swedish authorities’ work on earth observation data and for the development of AI-based analysis of data, generated in space systems. The platform is deployed on Kubernetes.

    Purpose

    • Increase the availability of space data for the benefit of developing society and industry

    • Provide platform for accessing space data and analytical tools

  • 32.
    Chronéer, Diana
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Ståhlbröst, Anna
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Habibipour, Abdolrasoul
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Urban Living Labs: Towards an Integrated Understanding of Their Key Components2019Inngår i: Technology Innovation Management Review, E-ISSN 1927-0321, Vol. 9, nr 3, s. 50-62Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    In today’s ongoing urbanization and escalating climate change, there is an increasing demand on cities to be innovative and inclusive to handle these emerging issues. As an answer to these challenges, and in order to generate and adopt sustainable innovations and nature-based solutions in the urban areas, the concept of urban living labs has emerged. However, to date, there is confusion concerning the concept of the urban living lab and its key components. Some interpret the urban living lab as an approach, others as a single project, and some as a specific place – and some just do not know. In order to unravel this complexity and better understand this concept, we sought to identify the key components of an urban living lab by discussing the perspective of city representatives in the context of an urban living lab project. To achieve this goal, we reviewed previous literature on this topic and carried out two workshops with city representatives, followed by an open-ended questionnaire. In this article, we identify and discuss seven key components of an urban living lab: governance and management structure; financing models; urban context; nature-based solutions; partners and users (including citizens); approach; and ICT and infrastructure. We also offer an empirically derived definition of the urban living lab concept.

    Fulltekst (pdf)
    fulltext
  • 33.
    Drajic, Dejan
    et al.
    DunavNET, Serbia.
    Andersson, Karl
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Datavetenskap.
    Zhang, Kai
    Martel Innovate, Switzerland.
    Stembert, Nathalie
    Stembert Design, Netherlands.
    Malmberg, Katariina
    European Network of Living Labs, Belgium.
    Brékine, Anna
    Mandat International, Switzerland.
    Vanobberghen, Wim
    imec – SMIT – vrije Universiteit Brussel, Belgium.
    Habibipour, Abdolrasoul
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Waeben, Jan
    imec – SMIT – vrije Universiteit Brussel, Belgium.
    User Engagement for Large Scale Pilots in the Internet of Things2019Inngår i: TELSIKS 2019: Proceedings of Papers / [ed] Prof. Dr. Bratislav D. Milovanović; Prof. Dr. Nebojša S. Dončov; Prof. Dr. Zoran Ž. Stanković; Dr. Tijana Ž. Dimitrijević; Dr. Biljana Stošić, 2019, s. 46-53Konferansepaper (Fagfellevurdert)
    Abstract [en]

    With an expected 50 billion connected devices by 2020, the Internet of Things (IoT) will reshape our environment with great economic opportunities. However, the IoT market evolution will depend directly on the end-user adoption, so it is necessary to support the Large Scale Pilots (LSPs) in order to actively engage end-users in the large scale pilot design, deployment and assessment. In this paper we are presenting end-user engagement methods, including co-creative workshops, crowdsourcing, Living Labs, and developed online tools and resources for end-user engagement, crowdsourcing and personal data protection.

    Fulltekst (pdf)
    fulltext
  • 34.
    Elgendy, Nada
    et al.
    M3S, Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland.
    Elragal, Ahmed
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Ohenoja, Markku
    Environmental and Chemical Engineering, Faculty of Technology, University of Oulu, Oulu, Finland.
    Päivärinta, Tero
    M3S, Faculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland.
    Ex-Post Evaluation of Data-Driven Decisions: Conceptualizing Design Objectives2022Inngår i: Perspectives in Business Informatics Research: 21st International Conference on Business Informatics Research, BIR 2022, Rostock, Germany, September 21–23, 2022, Proceedings, Springer Nature, 2022, s. 18-34Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper addresses a need for developing ex-post evaluation for data-driven decisions resulting in collaboration between humans and machines. As a first step of a design science project, we propose four design objectives for an ex-post evaluation solution, from the perspectives of both theory (concepts from the literature) and practice (through a case of industrial production planning): (1) incorporate multi-faceted decision evaluation criteria across the levels of environment, organization, and decision itself and (2) acknowledge temporal requirements of the decision contexts at hand, (3) define applicable mode(s) of collaboration between humans and machines to pursue collaborative rationality, and (4) enable a (potentially automated) feedback loop for learning from the (discrete or continuous) evaluations of past decisions. The design objectives contribute by supporting development of solutions for the observed lack of ex-post methods for evaluating data-driven decisions to enhance human-machine collaboration in decision making. Our future research involves design and implementation efforts through on-going industry-academia cooperation.

  • 35.
    Elgendy, Nada
    et al.
    aFaculty of Information Technology and Electrical Engineering, University of Oulu, Oulu, Finland.
    Elragal, Ahmed
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Päivärinta, Tero
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    DECAS: A Modern Data-Driven Decision Theory for Big Data and Analytics2022Inngår i: Journal of Decision Systems, ISSN 1246-0125, E-ISSN 2116-7052, Vol. 31, nr 4, s. 337-373Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Decisions continue to be an essential topic of utmost importance in every research field and era. However, while decision research has extensively offered a wide range of theories, it remains delved in the past, and needs robustness to sustain the future of data-driven decision-making, encompassing topics and technologies such as big data, analytics, machine learning, and automated decisions. Nowadays, decision processes have evolved, the role of humans as decision makers has changed and become inevitably intertwined with the support of machines, rationalities are no longer limited in the same way, data has become an abundant commodity, and the optimizing of decisions is not so far-fetched a tale as it once was in classical times. Accordingly, there is a dire need for new theories to support new phenomena. This paper aims to propose a modern data-driven decision theory, DECAS, to support the new elements of today’s decisions. Our theory extends upon classical decision theory by proposing three main claims: the (big) data and analytics should be considered as separate elements along with the decision-making process, the decision maker, and the decision; the appropriate collaboration between the decision maker and the analytics (machine) can result in a “collaborative rationality,” extending beyond the bounded rationality which decision makers were classically characterized by; and finally, the proper integration of the five elements, and the correct selection of data and analytics, can lead to more informed, and possibly better, decisions.  Hence, the theory is elaborated in the paper, and introduced to some data-driven decision examples.

    Fulltekst (pdf)
    fulltext
  • 36.
    Elgendy, Nada
    et al.
    M3S, Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland.
    Elragal, Ahmed
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Päivärinta, Tero
    M3S, Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland.
    Evaluating collaborative rationality-based decisions: a literature review2023Inngår i: CENTERIS – International Conference on ENTERprise Information Systems / ProjMAN – International Conference on Project MANagement / HCist – International Conference on Health and Social Care Information Systems and Technologies 2022 / [ed] Ricardo Martinho; Rui Rij; Maria Manuela Cruz-Cunha; Dulce Domingos; Emanuel Peres, Elsevier, 2023, s. 647-657Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Decision making has evolved throughout the years, nowadays harnessing massive amounts and types of data through the unprecedented capabilities of data science, analytics, machine learning, and artificial intelligence. This has potentially led to higher quality and more informed decisions based on the collaborative rationality between humans and machines, no longer bounded by the cognitive capacity and limited rationality of each on their own. However, the multiplicity of modes of collaboration and interaction between humans and machines has also increased the complexity of decision making, consequentially complicating ex-ante and ex-post decision evaluation. Nevertheless, evaluation remains crucial to enable human and machine learning, rationalization, and sensemaking. This paper addresses the need for more research on why and how to evaluate collaborative rationality-based decisions, setting the stage for future studies in developing holistic evaluation solutions. By analyzing four relevant streams of literature: 1) classical decision theory and organizational management, 2) cognitive and neuroscience, 3) AI and ML, and 4) data-driven decision making, we highlight the limitations of current literature in considering a holistic evaluation perspective. Finally, we elaborate the theoretical underpinnings from the knowledge base on how humans and machines evaluate decisions, and the considerations for evaluating collaborative rationality-based decisions.

    Fulltekst (pdf)
    fulltext
  • 37.
    Elragal, Ahmed
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Chroneer, Diana
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Andersson, Ingemar
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Ståhlbröst, Anna
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    The Parable of Robotics in Education: A State of the Art Report2019Inngår i: Bidrag från 7:e utvecklingskonferensen för Sveriges ingenjörsutbildningar / [ed] Lennart Pettersson; Karin Bolldén, Luleå tekniska universitet, 2019, s. 136-142Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Many technologies have been used in education. Such technologies fall under three main types: learning management systems; education data mining; and AI-enabled technologies. This report focuses on the use of robotics in interactive education. Over the past few years, interest in utilization of robotics in education has increased. Multiple attempts have been made, globally, in order to introduce robotics in education. Our report reveals that robotics have been used in education either in front scene acting as a teacher or in back scene supporting the teaching process. Our report also reveals that robotics are able to address unsolved educational issues such as achievement gaps and teachers gaps, in addition to the assistance it provides in some specific use cases. Further research efforts are indeed required to fully understand the exact role, current and future, or robotics in education. The report also introduce some challenges in using robotics in education e.g., Communication breakdowns, navigation capabilities, and the feeling of remote students

  • 38.
    Elragal, Ahmed
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Elgendy, Nada
    M3S, Faculty of Information Technology and Electrical Engineering, University of Oulu, Finland.
    A Data-Driven Decision-Making Readiness Assessment Model: The Case of a Swedish Food Manufacturer2024Inngår i: Decision Analytics Journal, ISSN 2772-6622, Vol. 10, artikkel-id 100405Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    This study proposes a model to assess data-driven decision-making (DDDM) readiness in organizations. We present the results from investigating the DDDM readiness of a Swedish organization in the food industry. We designed and developed a questionnaire to collect data about the organization’s decision-making and IT systems. We conducted eleven interviews at the case study organization: ten with various functional decision-makers and one with the IT Manager about IT systems. The interview data were then analyzed against known decision theories and state-of-the-art DDDM. Based on the interview outcomes, we analyze the data according to the assessment model and recommend changes to the organization’s readiness for data-driven decisions. The findings show that while the organization was assessed as ready in the decision-making process and decision-maker pillars, it was not ready in the data or analytics pillars. Accordingly, we recommend a set of actions, including considering integration and decision systems, further developing dashboards, increasing data and analytics resources (such as enterprise data warehouse, big data management tools, data lake environment, and data analytics algorithms), and defining key roles necessary for digitalization and DDDM (such as Data Engineer, Data Scientist, Business Intelligence Specialist, Chief Data Officer, and Data Warehouse Designer/Administrator). The contribution of this study is the DDDM readiness assessment model, accompanied by a questionnaire for determining the readiness level in organizations.

    Fulltekst (pdf)
    fulltext
  • 39.
    Elragal, Ahmed
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Haddara, Moutaz
    Department of Technology, Kristiania University College, Oslo, Norway.
    Design Science Research: Evaluation in the Lens of Big Data Analytics2019Inngår i: Systems, ISSN 2079-8954, Vol. 7, nr 2, artikkel-id 27Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Given the different types of artifacts and their various evaluation methods, one of the main challenges faced by researchers in design science research (DSR) is choosing suitable and efficient methods during the artifact evaluation phase. With the emergence of big data analytics, data scientists conducting DSR are also challenged with identifying suitable evaluation mechanisms for their data products. Hence, this conceptual research paper is set out to address the following questions. Does big data analytics impact how evaluation in DSR is conducted? If so, does it lead to a new type of evaluation or a new genre of DSR? We conclude by arguing that big data analytics should influence how evaluation is conducted, but it does not lead to the creation of a new genre of design research.

  • 40.
    Elragal, Ahmed
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Haddara, Moutaz
    Kristiania University College, Oslo, Norway.
    Hustad, Eli
    University of Agder, Kristiansand, Norway.
    Special Issue Editorial: Rejuvenating Enterprise Systems2020Inngår i: Scandinavian Journal of Information Systems, ISSN 0905-0167, E-ISSN 1901-0990, Vol. 32, nr 2, artikkel-id 5Artikkel i tidsskrift (Annet vitenskapelig)
  • 41.
    Elragal, Ahmed
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Hassanien, Hossam El-Din
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Augmenting Advanced Analytics into Enterprise Systems: A Focus on Post-Implementation Activities2019Inngår i: Systems, E-ISSN 2079-8954, Vol. 7, nr 2, artikkel-id 31Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    An analytics-empowered enterprise system looks to many organizations to be a far-fetched target, owing to the vast amounts of factors that need to be controlled across the implementation lifecycle activities, especially during usage and maintenance phases. On the other hand, advanced analytics techniques such as machine learning and data mining have been strongly present in academic as well as industrial arenas through robust classification and prediction. Correspondingly, this paper is set out to address a methodological approach that works on tackling post-live implementation activities, focusing on employing advanced analytics techniques to detect (business process) problems, find and recommend a solution to them, and confirm the solution. The objective is to make enterprise systems self-moderated by reducing the reliance on vendor support. The paper will profile an advanced analytics engine architecture fitted on top of an enterprise system to demonstrate the approach. Employing an advanced analytics engine has the potential to support post-implementation activities. Our research is innovative in two ways: (1) it enables enterprise systems to become self-moderated and increase their availability; and (2) the IT artifact i.e., the analytics engine, has the potential to solve other problems and be used by other systems, e.g., HRIS. This paper is beneficial to businesses implementing enterprise systems. It highlights how enterprise systems could be safeguarded from retirement caused by post-implementation problems.

    Fulltekst (pdf)
    fulltext
  • 42.
    Elragal, Rawan
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Elragal, Ahmed
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Habibipour, Abdolrasoul
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Food Analytics – A Literature Review and Ways Forward2024Inngår i: 2024 23rd International Symposium INFOTEH-JAHORINA (INFOTEH): Proceedings, 2024Konferansepaper (Fagfellevurdert)
  • 43.
    Elragal, Rawan
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Elragal, Ahmed
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Habibipour, Abdolrasoul
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Healthcare analytics—A literature review and proposed research agenda2023Inngår i: Frontiers in Big Data, ISSN 2624-909X, Vol. 6, artikkel-id 1277976Artikkel, forskningsoversikt (Fagfellevurdert)
    Abstract [en]

    This research addresses the demanding need for research in healthcare analytics, by explaining how previous studies have used big data, AI, and machine learning to identify, address, or solve healthcare problems. Healthcare science methods are combined with contemporary data science techniques to examine the literature, identify research gaps, and propose a research agenda for researchers, academic institutions, and governmental healthcare organizations. The study contributes to the body of literature by providing a state-of-the-art review of healthcare analytics as well as proposing a research agenda to advance the knowledge in this area. The results of this research can be beneficial for both healthcare science and data science researchers as well as practitioners in the field.

    Fulltekst (pdf)
    fulltext
  • 44.
    Ericson, Åsa
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Ek Styvén, Maria
    Luleå tekniska universitet, Institutionen för ekonomi, teknik, konst och samhälle, Industriell ekonomi.
    Zobel, Thomas
    Luleå tekniska universitet, Institutionen för ekonomi, teknik, konst och samhälle, Industriell ekonomi.
    Sustainable service offerings: The role of innovation awareness2023Inngår i: 31st Nordic Symposium on Tourism and Hospitality Research – Book of Abstracts / [ed] Anke Peters, Anna Hübner, Nataliia Godis, Parisa Setoodegan, Samudika Weliweriyage, Tatiana Chekalina, Östersund: ETOUR Research Centre, Mid Sweden University , 2023, s. 141-144Konferansepaper (Annet vitenskapelig)
    Fulltekst (pdf)
    fulltext
  • 45.
    Ericson, Åsa
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Lugnet, Johan
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Editorial2019Inngår i: International Journal of Product Development, ISSN 1477-9056, E-ISSN 1741-8178, Vol. 23, nr 4, s. 245-246Artikkel i tidsskrift (Annet vitenskapelig)
    Fulltekst (pdf)
    Editorial
  • 46.
    Ericson, Åsa
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Lugnet, Johan
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Ek Styvén, Maria
    Luleå tekniska universitet, Institutionen för ekonomi, teknik, konst och samhälle, Industriell ekonomi.
    Zobel, Thomas
    Luleå tekniska universitet, Institutionen för ekonomi, teknik, konst och samhälle, Industriell ekonomi.
    Crisis: a driver for tourism innovation and service design?2024Inngår i: DESIGN 2024, Cambridge University Press, 2024, s. 2343-2352Konferansepaper (Fagfellevurdert)
    Abstract [en]

    Tourism entrepreneurs adapted to regulations during COVID-19 by introducing creative solutions. The crisis was said to be a unique opportunity for innovating sustainable businesses. This study investigates crisis as a driver for innovation and its relevance to service design. Interviews with entrepreneurs are the empirical base that highlights that finetuning existing services is an established approach instead of innovative service design. Few expressed lessons were learned from the creative solutions made during the pandemic, suggesting that strategies for innovation need to be developed.

    Fulltekst (pdf)
    fulltext
  • 47.
    Ericson, Åsa
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Lugnet, Johan
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Silawiang, H.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Eliasson, L.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Wenngren, Johan
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    User-Centred Co-Design in the Pandemic - A Reindeer Case2022Inngår i: Proceedings of the Design Society, Cambridges Institutes Press, 2022, Vol. 2, s. 171-180Konferansepaper (Fagfellevurdert)
    Abstract [en]

    An innovative design process which has been conducted under the restrictions during the covid-19 pandemic is in focus in this study. Visits were banned so user-centred design activities were particularly challenged by the necessity to meet and work online. The purpose of the study is to present and reflect on how user-centred design was done under those conditions. The potential users, the reindeer herders, were merely contacted online, the user investigation were supported by for example using videos and storytelling. Also, the final concept is presented.

    Fulltekst (pdf)
    fulltext
  • 48.
    Ericson, Åsa
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Lugnet, Johan
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Solvang, Wei Deng
    Intelligent manufacturing and Logistics, UiT The Arctic University of Norway, Narvik, Norway .
    Kaartinen, Heidi
    Centria University of Applied Sciences, Research and Development, Ylivieska, Finland .
    Wenngren, Johan
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Challenges of Industry 4.0 in SME businesses2020Inngår i: 2020 3rd International Symposium on Small-scale Intelligent Manufacturing Systems (SIMS), IEEE, 2020Konferansepaper (Fagfellevurdert)
    Abstract [en]

    This paper problematizes and discuss a change towards new business models in order to highlight some basic challenges of digital transformation needed for Industry 4.0. A particular effort is to discuss if SMEs may have sufficient means to capitalize on such a new paradigm. As an approach to enable problematization and discussion, the paper presents concepts and standpoints that are related to the fourth industrial revolution. For example, the idea of new business models is a concept taken for granted, and is not sufficiently contextualized into digital transformation and Industry 4.0. The discussion sheds some light on the benefit of SMEs' flexibility, while simultaneously it finds mindsets challenges related to change and transformation as critical.

  • 49.
    Farooq, Umer
    et al.
    Department of Cyber Security, National University of Computer and Emerging Sciences, Islamabad 44000, Pakistan.
    Asim, Muhammad
    Department of Cyber Security, National University of Computer and Emerging Sciences, Islamabad 44000, Pakistan.
    Tariq, Noshina
    Department of Computer Science, Shaheed Zulfikar Ali Bhutto Institute of Science and Technology, Islamabad 44000, Pakistan.
    Baker, Thar
    Department of Computer Science, College of Computing and Informatics, University of Sharjah, Sharjah P.O. Box 27272, United Arab Emirates.
    Awad, Ali Ismail
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system. College of Information Technology, United Arab Emirates University, Al Ain P.O. Box 17551, United Arab Emirates; Faculty of Engineering, Al-Azhar University, Qena P.O. Box 83513, Egypt; Centre for Security, Communications and Network Research, University of Plymouth, Plymouth PL4 8AA, UK.
    Multi-Mobile Agent Trust Framework for Mitigating Internal Attacks and Augmenting RPL Security2022Inngår i: Sensors, E-ISSN 1424-8220, Vol. 22, nr 12, artikkel-id 4539Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Recently, the Internet of Things (IoT) has emerged as an important way to connect diverse physical devices to the internet. The IoT paves the way for a slew of new cutting-edge applications. Despite the prospective benefits and many security solutions offered in the literature, the security of IoT networks remains a critical concern, considering the massive amount of data generated and transmitted. The resource-constrained, mobile, and heterogeneous nature of the IoT makes it increasingly challenging to preserve security in routing protocols, such as the routing protocol for low-power and lossy networks (RPL). RPL does not offer good protection against routing attacks, such as rank, Sybil, and sinkhole attacks. Therefore, to augment the security of RPL, this article proposes the energy-efficient multi-mobile agent-based trust framework for RPL (MMTM-RPL). The goal of MMTM-RPL is to mitigate internal attacks in IoT-based wireless sensor networks using fog layer capabilities. MMTM-RPL mitigates rank, Sybil, and sinkhole attacks while minimizing energy and message overheads by 25–30% due to the use of mobile agents and dynamic itineraries. MMTM-RPL enhances the security of RPL and improves network lifetime (by 25–30% or more) and the detection rate (by 10% or more) compared to state-of-the-art approaches, namely, DCTM-RPL, RBAM-IoT, RPL-MRC, and DSH-RPL. 

  • 50.
    Fridlund, Angelica
    et al.
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Tunudd, Emil
    Luleå tekniska universitet, Institutionen för system- och rymdteknik, Digitala tjänster och system.
    Digitalt lärande - ett hinder eller en möjlighet?: Om digital kompetens inom distansutbildningar2022Independent thesis Basic level (degree of Bachelor), 10 poäng / 15 hpOppgave
    Abstract [en]

    This study is based on an abductive approach and a case study where the purpose is to investigate how students experience online education. The questions to answer in the study are based on the students' experiences of expected digital prior knowledge, their need for digital literacy, what advantages and disadvantages they saw with the digital environments and tools they used and what improvements could be identified. The empirical process was done through semi-structured interviews with thematic analysis as an analysis method, where it emerged that the students felt that there is an expectation from universities that the students should have a digital knowledge prior to attending the university and that they had to be able to work independently. There were shortcomings in the knowledge about the digital tools with the teachers and that meant inadequate support for the students. Despite this, the students were satisfied with their choices to study online, but they had many suggestions for improvement measures, which leaves room for future research linked to the issues.

    Fulltekst (pdf)
    Examensarbete Emil Tunudd Angelica Fridlund
1234 1 - 50 of 173
RefereraExporteraLink til resultatlisten
Permanent link
Referera
Referensformat
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Annet format
Fler format
Språk
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Annet språk
Fler språk
Utmatningsformat
  • html
  • text
  • asciidoc
  • rtf