Change search
Refine search result
12 1 - 50 of 81
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 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 University of Technology, Department of Computer Science, Electrical and Space Engineering, Digital Services and Systems. 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 Images2019In: IEEE-ICM 2019 CAIRO-EGYPT: The 31st International Conference on Microelectronics, IEEE, 2019, p. 304-307Conference paper (Other academic)
    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.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Khalaf, Ashraf A. M.
    Minia University, Egypt.
    Hamed, Hesham F. A.
    Minia University, Egypt.
    Classification of Brain Tumor MRIs Using a Kernel Support Vector Machine2016In: Building Sustainable Health Ecosystems: 6th International Conference on Well-Being in the Information Society, WIS 2016, Tampere, Finland, September 16-18, 2016, Proceedings / [ed] Hongxiu Li, Pirkko Nykänen, Reima Suomi, Nilmini Wickramasinghe, Gunilla Widén, Ming Zhan, Springer International Publishing , 2016, p. 151-160Conference paper (Refereed)
    Abstract [en]

    The use of medical images has been continuously increasing, which makes manual investigations of every image a difficult task. This study focuses on classifying brain magnetic resonance images (MRIs) as normal, where a brain tumor is absent, or as abnormal, where a brain tumor is present. A hybrid intelligent system for automatic brain tumor detection and MRI classification is proposed. This system assists radiologists in interpreting the MRIs, improves the brain tumor diagnostic accuracy, and directs the focus toward the abnormal images only. The proposed computer-aided diagnosis (CAD) system consists of five steps: MRI preprocessing to remove the background noise, image segmentation by combining Otsu binarization and K-means clustering, feature extraction using the discrete wavelet transform (DWT) approach, and dimensionality reduction of the features by applying the principal component analysis (PCA) method. The major features were submitted to a kernel support vector machine (KSVM) for performing the MRI classification. The performance evaluation of the proposed system measured a maximum classification accuracy of 100 % using an available MRIs database. The processing time for all processes was recorded as 1.23 seconds. The obtained results have demonstrated the superiority of the proposed system.

  • 3.
    Abd-Ellah, Mahmoud Khaled
    et al.
    Al-Madina Higher Institute for Engineering and Technology, Giza, Egypt .
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Digital Services and Systems. 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 Imaging2020In: Deep Learning in Computer Vision: Principles and Applications / [ed] Mahmoud Hassaballah, Ali Ismail Awad, CRC Press, 2020, 1st, p. 233-260Chapter in book (Other academic)
  • 4.
    Abd-Ellah, Mahmoud Khaled
    et al.
    Electronic and Communication Department Al-Madina Higher Institute for Engineering and Technology, Giza.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Khalaf, Ashraf A. M.
    Faculty of Engineering, Minia University.
    Hamed, Hesham F. A.
    Faculty of Engineering, Minia University.
    Design and implementation of a computer-aided diagnosis system for brain tumor classification2017In: 2016 28th International Conference on Microelectronics (ICM), 2017, p. 73-76, article id 7847911Conference paper (Refereed)
    Abstract [en]

    Computer-aided diagnosis (CAD) systems have become very important for the medical diagnosis of brain tumors. The systems improve the diagnostic accuracy and reduce the required time. In this paper, a two-stage CAD system has been developed for automatic detection and classification of brain tumor through magnetic resonance images (MRIs). In the first stage, the system classifies brain tumor MRI into normal and abnormal images. In the second stage, the type of tumor is classified as benign (Noncancerous) or malignant (Cancerous) from the abnormal MRIs. The proposed CAD ensembles the following computational methods: MRI image segmentation by K-means clustering, feature extraction using discrete wavelet transform (DWT), feature reduction by applying principal component analysis (PCA). The two-stage classification has been conducted using a support vector machine (SVM). Performance evaluation of the proposed CAD has achieved promising results using a non-standard MRIs database.

  • 5.
    Abd-Ellah, Mahmoud Khaled
    et al.
    Electronics and Communications Department, Al-Madina Higher Institute for Engineering and Technology, Giza, Egypt.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Faculty of Engineering, Al-Azhar University, Qena, Egypt.
    Khalaf, Ashraf A.M.
    Electronics and Communications Department, Faculty of Engineering, Minia University, Minia, Egypt.
    Hamed, Hesham F.A.
    Electronics and Communications Department, Faculty of Engineering, Minia University, Minia, Egypt.
    A Review on Brain Tumor Diagnosis from MRI Images: Practical Implications, Key Achievements, and Lessons Learned2019In: Magnetic Resonance Imaging, ISSN 0730-725X, E-ISSN 1873-5894, Vol. 61, p. 300-318Article in journal (Refereed)
    Abstract [en]

    The successful early diagnosis of brain tumors plays a major role in improving the treatment outcomes and thus improving patient survival. Manually evaluating the numerous magnetic resonance imaging (MRI) images produced routinely in the clinic is a difficult process. Thus, there is a crucial need for computer-aided methods with better accuracy for early tumor diagnosis. Computer-aided brain tumor diagnosis from MRI images consists of tumor detection, segmentation, and classification processes. Over the past few years, many studies have focused on traditional or classical machine learning techniques for brain tumor diagnosis. Recently, interest has developed in using deep learning techniques for diagnosing brain tumors with better accuracy and robustness. This study presents a comprehensive review of traditional machine learning techniques and evolving deep learning techniques for brain tumor diagnosis. This review paper identifies the key achievements reflected in the performance measurement metrics of the applied algorithms in the three diagnosis processes. In addition, this study discusses the key findings and draws attention to the lessons learned as a roadmap for future research.

  • 6.
    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 University of Technology, Department of Computer Science, Electrical and Space Engineering, Digital Services and Systems. 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 Segmentation2019In: 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, p. 106-116Conference paper (Refereed)
    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.

  • 7.
    Alani, Mohammed M.
    et al.
    Seneca College, Toronto, Canada.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Digital Services and Systems. 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 Learning2022In: Computers & security (Print), ISSN 0167-4048, E-ISSN 1872-6208, Vol. 117, article id 102718Article in journal (Refereed)
    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 University of Technology, Department of Computer Science, Electrical and Space Engineering, Digital Services and Systems. 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 Things2023In: IEEE Transactions on Industrial Informatics, ISSN 1551-3203, E-ISSN 1941-0050, Vol. 19, no 1, p. 683-692Article in journal (Refereed)
    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 University of Technology, Department of Computer Science, Electrical and Space Engineering, Digital Services and Systems. 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 System2022In: IEEE Access, E-ISSN 2169-3536, Vol. 10, p. 73214-73228Article in journal (Refereed)
    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.
    Ali, Bako
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Faculty of Engineering, Al Azhar University.
    Cyber and Physical Security Vulnerability Assessment for IoT-Based Smart Homes2018In: Sensors, E-ISSN 1424-8220, Vol. 18, no 3, article id 817Article in journal (Refereed)
    Abstract [en]

    The Internet of Things (IoT) is an emerging paradigm focusing on the connection of devices, objects, or “things” to each other, to the Internet, and to users. IoT technology is anticipated to become an essential requirement in the development of smart homes, as it offers convenience and efficiency to home residents so that they can achieve better quality of life. Application of the IoT model to smart homes, by connecting objects to the Internet, poses new security and privacy challenges in terms of the confidentiality, authenticity, and integrity of the data sensed, collected, and exchanged by the IoT objects. These challenges make smart homes extremely vulnerable to different types of security attacks, resulting in IoT-based smart homes being insecure. Therefore, it is necessary to identify the possible security risks to develop a complete picture of the security status of smart homes. This article applies the operationally critical threat, asset, and vulnerability evaluation (OCTAVE) methodology, known as OCTAVE Allegro, to assess the security risks of smart homes. The OCTAVE Allegro method focuses on information assets and considers different information containers such as databases, physical papers, and humans. The key goals of this study are to highlight the various security vulnerabilities of IoT-based smart homes, to present the risks on home inhabitants, and to propose approaches to mitigating the identified risks. The research findings can be used as a foundation for improving the security requirements of IoT-based smart homes.

  • 11.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Aktivitet: Workshop on Emerging Aspects in Information Security2015Other (Other (popular science, discussion, etc.))
  • 12.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Aktivitet: Communications in Computer and Information Science, Vol. 3812013Other (Other (popular science, discussion, etc.))
  • 13.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Aktivitet: Computer Networks2011Other (Other (popular science, discussion, etc.))
    Abstract [en]

    The International Journal of Computer and Telecommunications Networking (COMNET)

  • 14.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Aktivitet: Computers & Security2014Other (Other (popular science, discussion, etc.))
  • 15.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Aktivitet: Concurrency and Computation: Practice and Experience2016Other (Other (popular science, discussion, etc.))
  • 16.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Aktivitet: Health Information Science and Systems (HISS)2016Other (Other (popular science, discussion, etc.))
  • 17.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Aktivitet: Information Sciences2013Other (Other (popular science, discussion, etc.))
  • 18.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Aktivitet: Intelligent Systems Reference Library, Vol. 702014Other (Other (popular science, discussion, etc.))
  • 19.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Aktivitet: International Conference on Mobile and Ubiquitous Systems2013Other (Other (popular science, discussion, etc.))
  • 20.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Aktivitet: International Conference on Telecommunications2016Other (Other (popular science, discussion, etc.))
  • 21.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Aktivitet: International Conference on Telecommunications2016Other (Other (popular science, discussion, etc.))
  • 22.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Aktivitet: International Japan-Egypt Conference on Electronics, Communications and Computers2013Other (Other (popular science, discussion, etc.))
  • 23.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Aktivitet: International Journal of Computational Vision and Robotics. Special Issue on: "Advances in Soft Computing Techniques for Image Processing"2013Other (Other (popular science, discussion, etc.))
    Abstract [en]

    Vol. 3, No. 4

  • 24.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Aktivitet: Pattern Recognition2014Other (Other (popular science, discussion, etc.))
    Abstract [en]

    The Journal of the Pattern Recognition Society

  • 25.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Aktivitet: Science and Information Conference2014Other (Other (popular science, discussion, etc.))
  • 26.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Aktivitet: Science and Information Conference2013Other (Other (popular science, discussion, etc.))
  • 27.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Aktivitet: SpringerPlus2015Other (Other (popular science, discussion, etc.))
  • 28.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Aktivitet: Studies in Computational Intelligence, Vol. 6302016Other (Other (popular science, discussion, etc.))
  • 29.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Aktivitet: The 28th International Conference on Image and Vision Computing New Zealand (IVCNZ 2013)2013Other (Other (popular science, discussion, etc.))
  • 30.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Aktivitet: The 29th International Conference on Image and Vision Computing New Zealand (IVCNZ 2014)2014Other (Other (popular science, discussion, etc.))
  • 31.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Aktivitet: The 30th International Conference on Image and Vision Computing New Zealand ( (IVCNZ 2015)2015Other (Other (popular science, discussion, etc.))
  • 32.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Aktivitet: The 31st International Conference Image and Vision Computing New Zealand (IVCNZ 2016)2016Other (Other (popular science, discussion, etc.))
  • 33.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Aktivitet: The 49th Hawaii International Conference on System Sciences (HICSS-49)2016Other (Other (popular science, discussion, etc.))
  • 34.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Aktivitet: Workshop on Emerging Aspects in Information Security2014Other (Other (popular science, discussion, etc.))
  • 35.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Faculty of Engineering, Al Azhar University, Qena, Egypt.
    Fast Fingerprint Orientation Field Estimation Incorporating General Purpose GPU2016In: Soft Computing Applications: Proceedings of the 6th International Workshop Soft Computing Applications (SOFA 2014) / [ed] Valentina Emilia Balas; Lakhmi C. Jain; Branko Kovačević, New York: Encyclopedia of Global Archaeology/Springer Verlag, 2016, Vol. 2, p. 891-902Conference paper (Refereed)
    Abstract [en]

    Fingerprint is one of the broadly utilized biometric traits for personal identification in both civilian and forensic applications due to its high acceptability, strong security, and low cost. Fingerprint ridge orientation is one of the global fingerprint representations that keeps the holistic ridge structure in a small storage area. The importance of fingerprint ridge orientation comes from its usage in fingerprint singular point detection, coarse level classification, and fingerprint alignment. However, processing time is an important factor in any automatic fingerprint identification system, estimating that ridge orientation image may consume long processing time. This research presents an efficient ridge orientation estimation approach by incorporating a Graphics Processing Unit (GPU) capability to the traditional pixel gradient method. The simulation work shows a significant enhancement in ridge orientation estimation time by 6.41x using a general purpose GPU in comparison to the CPU execution.

  • 36.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Fingerprint local invariant feature extraction on GPU with CUDA2013In: Informatica, ISSN 0350-5596, E-ISSN 1854-3871, Vol. 37, no 3, p. 279-284Article in journal (Refereed)
    Abstract [en]

    Driven from its uniqueness, immutability, acceptability, and low cost, fingerprint is in a forefront between biometric traits. Recently, the GPU has been considered as a promising parallel processing technology due to its high performance computing, commodity, and availability. Fingerprint authentication is keep growing, and includes the deployment of many image processing and computer vision algorithms. This paper introduces the fingerprint local invariant feature extraction using two dominant detectors, namely SIFT and SURF, which are running on the CPU and the GPU. The paper focuses on the consumed time as an important factor for fingerprint identification. The experimental results show that the GPU implementations produce promising behaviors for both SIFT and SURF compared to the CPU one. Moreover, the SURF feature detector provides shorter processing time compared to the SIFT CPU and GPU implementations.

    Download full text (pdf)
    FULLTEXT01
  • 37.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    From classical methods to animal biometrics: A review on cattle identification and tracking2016In: Computers and Electronics in Agriculture, ISSN 0168-1699, E-ISSN 1872-7107, Vol. 123, p. 423-435Article in journal (Refereed)
    Abstract [en]

    Cattle, buffalo and cow, identification has recently played an influential role towards understanding disease trajectory, vaccination and production management, animal traceability, and animal ownership assignment. Cattle identification and tracking refers to the process of accurately recognizing individual cattle and their products via a unique identifier or marker. Classical cattle identification and tracking methods such as ear tags, branding, tattooing, and electrical methods have long been in use; however, their performance is limited due to their vulnerability to losses, duplications, fraud, and security challenges. Owing to their uniqueness, immutability, and low costs, biometric traits mapped into animal identification systems have emerged as a promising trend. Biometric identifiers for beef animals include muzzle print images, iris patterns, and retinal vascular patterns. Although using biometric identifiers has replaced human experts with computerized systems, it raises additional challenges in terms of identifier capturing, identification accuracy, processing time, and overall system operability. This article reviews the evolution in cattle identification and tracking from classical methods to animal biometrics. It reports on traditional animal identification methods and their advantages and problems. Moreover, this article describes the deployment of biometric identifiers for effectively identifying beef animals. The article presents recent research findings in animal biometrics, with a strong focus on cattle biometric identifiers such as muzzle prints, iris patterns, and retinal vascular patterns. A discussion of current challenges involved in the biometric-based identification systems appears in the conclusions, which may drive future research directions.

  • 38.
    Awad, Ali Ismail
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Introduction to Information Security Foundations and Applications2018In: Information Security: Foundations, technologies and applications / [ed] Ali Ismail Awad and Michael Fairhurst, Institution of Engineering and Technology, 2018, , p. 432p. 3-9Chapter in book (Refereed)
    Abstract [en]

    Information security has extended to include several research directions like user authentication and authorization, network security, hardware security, software security, and data cryptography. Information security has become a crucial need for protecting almost all information transaction applications. Security is considered as an important science discipline whose many multifaceted complexities deserve the synergy of the computer science and engineering communities.

    Download full text (pdf)
    fulltext
  • 39.
    Awad, Ali Ismail
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Digital Services and Systems. United Arab Emirates University, Al Ain, United Arab Emirates; Al-Azhar University, Qena, Egypt.
    Abawajy, Jemal
    Deakin University, Australia.
    Preface2022In: Security and Privacy in the Internet of Things: Architectures, Techniques, and Applications / [ed] Ali Ismail Awad; Jemal Abawajy, John Wiley & Sons, 2022, p. xix-xxiiiChapter in book (Other academic)
  • 40.
    Awad, Ali Ismail
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Digital Services and Systems. 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 (editor) (Other academic)
    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.

  • 41.
    Awad, Ali Ismail
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Digital Services and Systems. United Arab Emirates University, Al Ain, United Arab Emirates; Al-Azhar University, Qena, Egypt.
    Abawajy, JemalDeakin University, Australia.
    Security and Privacy in the Internet of Things: Architectures, Techniques, and Applications2022Collection (editor) (Other academic)
  • 42.
    Awad, Ali Ismail
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Andrzej, BialasInstitute of Innovative Technologies EMAG.
    Proceedings of the 2017 Federated Conference on Computer Science and Information Systems (FedCSIS 2017)2017Conference proceedings (editor) (Refereed)
    Abstract [en]

    https://fedcsis.org/2017/insert

  • 43.
    Awad, Ali Ismail
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Fairhurst, MichaelSchool of Engineering and Digital Arts at the University of Kent.
    Information Security: Foundations, technologies and applications2018Collection (editor) (Refereed)
    Abstract [en]

    The rapid advancements in telecommunications, computing hardware and software, and data encryption, and the widespread use of electronic data processing and electronic business conducted through the Internet have led to a strong increase in information security threats. The latest advances in information security have increased practical deployments and scalability across a wide range of applications to better secure and protect our information systems and the information stored, processed and transmitted. This book outlines key emerging trends in information security from the foundations and technologies in biometrics, cybersecurity, and big data security to applications in hardware and embedded systems security, computer forensics, the Internet of Things security, and network security. Information Security: Foundations, technologies and applications is a comprehensive review of cutting-edge algorithms, technologies, and applications, and provides new insights into a range of fundamentally important topics in the field. This up-to-date body of knowledge is essential reading for researchers and advanced students in information security, and for professionals in sectors where information security is required.

  • 44.
    Awad, Ali Ismail
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Furnell, Steven
    Centre for Security, Communications & Network Research, Plymouth University.
    Hassan, Abbas M.
    Faculty of Engineering, Qena, Al-Azhar University, Egypt.
    Tryfonas, Theo
    University of Bristol, United Kingdom.
    Special issue on security of IoT-enabled infrastructures in smart cities2019In: Ad hoc networks, ISSN 1570-8705, E-ISSN 1570-8713, Vol. 92, article id 101850Article in journal (Other academic)
  • 45.
    Awad, Ali Ismail
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Digital Services and Systems. 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.
    Preface2021In: Security in Cyber-Physical Systems: Foundations and Applications / [ed] Ali Ismail Awad; Steven Furnell; Marcin Paprzycki; Sudhir Kumar Sharma, Springer Nature, 2021, Vol. 339, p. v-ixChapter in book (Other academic)
  • 46.
    Awad, Ali Ismail
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Furnell, StevenSchool of Computer Science, University of Nottingham, Nottingham, UK.Paprzycki, MarcinSystems Research Institute, Polish Academy of Sciences, Warszawa, Poland.Sharma, Sudhir KumarInstitute of Information Technology and Management, New Delhi, India.
    Security in Cyber-Physical Systems: Foundations and Applications2021Collection (editor) (Refereed)
    Abstract [en]

    This book is a relevant reference for any readers interested in the security aspects of Cyber-Physical Systems and particularly useful for those looking to keep informed on the latest advances in this dynamic area.

    Cyber-Physical Systems (CPSs) are characterized by the intrinsic combination of software and physical components. Inherent elements often include wired or wireless data communication, sensor devices, real-time operation and automated control of physical elements. Typical examples of associated application areas include industrial control systems, smart grids, autonomous vehicles and avionics, medial monitoring and robotics. The incarnation of the CPSs can therefore range from considering individual Internet-of-Things devices through to large-scale infrastructures.  

    Presented across ten chapters authored by international researchers in the field from both academia and industry, this book offers a series of high-quality contributions that collectively address and analyze the state of the art in the security of Cyber-Physical Systems and related technologies. The chapters themselves include an effective mix of theory and applied content, supporting an understanding of the underlying security issues in the CPSs domain, alongside related coverage of the technological advances and solutions proposed to address them. The chapters comprising the later portion of the book are specifically focused upon a series of case examples, evidencing how the protection concepts can translate into practical application. 

  • 47.
    Awad, Ali Ismail
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Faculty of Engineering, Al-Azhar University, Qena, Egypt. Centre for Security, Communications and Network Research, University of Plymouth, Plymouth, UK.
    Hassaballah, M.
    Department of Computer Science, Faculty of Computers and Information, South Valley University, Qena, Egypt.
    Bag-of-Visual-Words for Cattle Identification from Muzzle Print Images2019In: Applied Sciences, E-ISSN 2076-3417, Vol. 9, no 22, article id 4914Article in journal (Refereed)
    Abstract [en]

    Cattle, buffalo and cow identification plays an influential role in cattle traceability from birth to slaughter, understanding disease trajectories and large-scale cattle ownership management. Muzzle print images are considered discriminating cattle biometric identifiers for biometric-based cattle identification and traceability. This paper presents an exploration of the performance of the bag-of-visual-words (BoVW) approach in cattle identification using local invariant features extracted from a database of muzzle print images. Two local invariant feature detectors—namely, speeded-up robust features (SURF) and maximally stable extremal regions (MSER)—are used as feature extraction engines in the BoVW model. The performance evaluation criteria include several factors, namely, the identification accuracy, processing time and the number of features. The experimental work measures the performance of the BoVW model under a variable number of input muzzle print images in the training, validation, and testing phases. The identification accuracy values when utilizing the SURF feature detector and descriptor were 75%, 83%, 91%, and 93% for when 30%, 45%, 60%, and 75% of the database was used in the training phase, respectively. However, using MSER as a points-of-interest detector combined with the SURF descriptor achieved accuracies of 52%, 60%, 67%, and 67%, respectively, when applying the same training sizes. The research findings have proven the feasibility of deploying the BoVW paradigm in cattle identification using local invariant features extracted from muzzle print images. 

  • 48.
    Awad, Ali Ismail
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hassaballah, MahmoudImage and Video Processing Lab, Faculty, South Valley University.
    Image Feature Detectors and Descriptors: Foundations and Applications2016Collection (editor) (Refereed)
  • 49.
    Awad, Ali Ismail
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Faculty of Engineering, Al Azhar University, Qena, Egypt.
    Hassanien, Aboul Ella
    Faculty of Computers & Information Cairo University, Cairo, Egypt.
    Impact of Some Biometric Modalities on Forensic Science2014In: Computational Intelligence in Digital Forensics: Forensic Investigation and Applications, Springer International Publishing , 2014, p. 47-62Chapter in book (Refereed)
    Abstract [en]

    Recently, forensic science has had many challenges in many different types of crimes and crime scenes, vary from physical crimes to cyber or computer crimes. Accurate and efficient human identification or recognition have become crucial for forensic applications due to the large diversity of crime scenes, and because of the increasing need to accurately identify criminals from the available crime evidences. Biometrics is an emerging technology that provides accurate and highly secure personal identification and verification systems for civilian and forensic applications. The positive impact of biometric modalities on forensic science began with the rapid developments in computer science, computational intelligence, and computing approaches. These advancements have been reflected in the biometric modality capturing process, feature extraction, feature robustness, and features matching. A complete and automatic biometric identification or recognition systems have been built accordingly. This chapter presents a study of the impacts of using some biometric modalities in forensic applications. Although biometrics identification replaces human work with computerized and automatic systems in order to achieve better performance, new challenges have arisen. These challenges lie in biometric system reliability and accuracy, system response time, data mining and classification, and protecting user privacy. This chapter sheds light on the positive and the negative impacts of using some biometric modalities in forensic science. In particular, the impacts of fingerprint image, facial image, and iris patterns are considered. The selected modalities are covered preliminarily before tackling their impact on forensic applications. Furthermore, an extensive look at the future of biometric modalities deployment in forensic applications is covered as the last part of the chapter.

  • 50.
    Awad, Ali Ismail
    et al.
    Faculty of Engineering, Faculty of Engineering, Al Azhar University, Qena, Egypt.
    Hassanien, Aboul EllaCairo University.Baba, KensukeKyushu University.
    Advances in Security of Information and Communication Networks: First International Conference, SecNet 2013, Cairo, Egypt, September 3-5, 2013. Proceedings2013Collection (editor) (Refereed)
12 1 - 50 of 81
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf