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  • 1.
    Booth, Todd
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Luleå University of Technology, Centre for Critical Infrastructure and Societal Security.
    Stronger Authentication for Password Credential Internet Services2017In: Proceedings of the 2017 Third Conference on Mobile and Secure Services (MOBISECSERV) / [ed] Pascal Urien, Selwyn Piramuthu, Piscataway, NJ: IEEE conference proceedings, 2017, p. 41-45, article id 7886566Conference paper (Refereed)
    Abstract [en]

    Most Web and other on-line service providers (”Inter- net Services”) only support legacy ID (or email) and password (ID/PW) credential authentication. However, there are numerous vulnerabilities concerning ID/PW credentials. Scholars and the industry have proposed several improved security solutions, such as MFA, however most of the Internet Services have refused to adopt these solutions. Mobile phones are much more sensitive to these vulnerabilities (so this paper focuses on mobile phones). Many users take advantage of password managers, to keep track of all their Internet Service profiles. However, the Internet Service profiles found in password managers, are normally kept on the PC or mobile phone’s disk, in an encrypted form. Our first contribution is a design guideline, whereby the Internet Service profiles never need to touch the client’s disk. Most users would benefit, if they had the ability to use MFA, to login to a legacy Internet Service, which only supports ID/PW credential authentication. Our second contribution is a design guideline, whereby users can choose, for each legacy ID/PW Internet Service, which specific MFA they wish to use. We have also presenting conceptual design guidelines, showing that both of our contributions are minor changes to existing password managers, which can be implemented easily with low overhead.

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  • 2.
    Islam, Raihan Ul
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Luleå University of Technology, Centre for Critical Infrastructure and Societal Security.
    Hossain, Mohammad Shahadat
    Department of Computer Science and Engineering, University of Chittagong.
    Network Intelligence for Enhanced Multi-Access Edge Computing (MEC) in 5G2019Conference paper (Other academic)
    Abstract [en]

    5G networks will enable people and machines to communicate at high speeds and very low latencies, in a reliable way. This opens up opportunities for totally new usage patterns and the fully connected Industry 4.0-enabled enterprise covering the entire value chain from design, production, deployment, to usage of products. 5G will be rolled out across the whole world, including Sweden where the first 5G test network was launched late 2018. One important new feature in 5G is the emerging edge computing capabilities, where users can easily offload computational tasks to the network’s edge very close to the user. At the same time, computational tasks traditionally performed in central nodes can be offloaded from remotely located data centres to the network’s edge. Multi-access Edge Computing (MEC) is a promising network architecture delivering solutions along these lines offering a platform for applications with requirements on low latencies and high reliability. This paper targets this environment with a novel Belief-rule-based (BRB) unsupervised learning algorithm for clustering helping 5G applications to take intelligent decisions on software deployment. The scenarios consist of different combinations of numbers of users and connections and mobility patterns. The target environment is built up using a three-tier structure with a container-based solution where software components can easily be spread around the network.

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  • 3.
    Islam, Raihan Ul
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hossain, Mohammad Shahadat
    Department of Computer Science and Engineering, University of Chittagong.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Luleå University of Technology, Centre for Critical Infrastructure and Societal Security.
    A learning mechanism for BRBES using enhanced Belief Rule-Based Adaptive Differential Evolution2020Conference paper (Refereed)
    Abstract [en]

    Nowadays, Belief rule-based expert systems (BRBESs) are widely used in various domains which provides a framework to handle qualitative and quantitative data by addressing several kinds of uncertainty. Learning plays an important role in BRBES to upgrade its knowledge base and parameters values, necessary for the improvement of  the prediction accuracy. Different optimal training procedures such as Particle Swarm Optimisation (PSO), Differential Evolution (DE), and Genetic Algorithm (GA) have been used as learning mechanisms. Among these procedures, DE performs comparatively better than others. However, DE's performance depends significantly in assigning near optimal values to its control parameters including cross over and mutation factors. Therefore, the objective of this article is to present a novel optimal training procedure by integrating DE with BRBES. This is named as enhanced belief rule-based adaptive differential evolution (eBRBaDE) algorithm because it has the ability to determine the near-optimal values of both the control parameters while ensuring the balanced exploitation and exploration in the search space.  In addition, a new joint optimization learning mechanism by using eBRBaDE is presented where both parameter and structure of BRBES are considered.  The reliability of the eBRBaDE has been compared with evolutionary optimization algorithms such as GA, PSO, BAT, DE and L-SHADE. This comparison has been carried out by taking account of both conjunctive and disjunctive BRBESs while predicting the Power Usage Effectiveness (PUE) of a datacentre. The comparison demonstrates that the eBRBaDE provides higher prediction accuracy of PUE than from other evolutionary optimization algorithms.

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  • 4.
    Islam, Raihan Ul
    et al.
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.
    Hossain, Mohammad Shahadat
    Department of Computer Science and Engineering, University of Chittagong.
    Andersson, Karl
    Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science. Luleå University of Technology, Centre for Critical Infrastructure and Societal Security.
    Inference and Multi-level Learning in a Belief Rule-Based Expert System to Predict Flooding2020Conference paper (Refereed)
    Abstract [en]

    Floods are one of the most dangerous catastrophic events. By the year 2050 flooding due to rise of ocean level  may  cost  one  trillion USD to coastal cities. Since flooding involves multi-dimensional elements, its accurate prediction is difficult. In addition, the elements cannot be measured with 100% accuracy. Belief rule-based expert systems (BRBESs) can be considered as an appropriate approach to handle  this  type  of  problem  because they are capable of addressing  uncertainty. However, BRBESs need to be equipped with the capacity to handle multi- level learning and inference to improve its accuracy of flood prediction. Therefore, this paper proposes a new learning and inference mechanism, named joint optimization using belief rule- based adaptive differential evolution (BRBaDE) for multi-level BRBES, which has the capability to handle multi-level learning and inference. Various machine learning methods, including Artificial Neural Networks (ANN), Support Vector Machine (SVM), Linear Regression and Long Short Term Memory have been compared with BRBaDE. The result exhibits that our proposed learning mechanism performs betters than learning techniques as mentioned above in terms of accuracy in flood prediction.

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1 - 4 of 4
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