Vol. 25, No. 2

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Now showing 1 - 4 of 4
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    Minimum-Volume Covering Ellipsoids: Improving the Efficiency of the Wolfe-Atwood Algorithm for Large-Scale Instances by Pooling and Batching
    (Institute of Automation and Computer Science, Brno University of Technology, 2019-12-20) Kudela, Jakub
    The Minimum-Volume Covering Ellipsoid (MVCE) problem is an important optimization problem that comes up in various areas of engineering and statistics. In this paper, we improve the state-of-the-art Wolfe-Atwood algorithm for solving the MVCE problem with pooling and batching procedures. This implementation yields significant improvements on the runtime of the algorithm for large-scale instances of the MVCE problem, which is demonstrated on quite extensive computational experiments.
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    A Survey on Artificial Intelligence in Malware as Next-Generation Threats
    (Institute of Automation and Computer Science, Brno University of Technology, 2019-12-20) Thanh, Cong Truong; Zelinka, Ivan
    Recent developments in Artificial intelligence (AI) have a vast transformative potential for both cybersecurity defenders and cybercriminals. Anti-malware solutions adopt intelligent techniques to detect and prevent threats to the digital space. In contrast, cybercriminals are aware of the new prospects too and will probably try to use it in their activities. This survey aims at providing an overview on the way artificial intelligence can be used to power a malicious program that is: intelligent evasion techniques, autonomous malware, AI against itself, and applying bio-inspired computation and swarm intelligence.
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    How to Burn a Network or Spread Alarm
    (Institute of Automation and Computer Science, Brno University of Technology, 2019-12-20) Simon, Marek; Huraj, Ladislav; Dirgova Luptakova, Iveta; Pospichal, Jiri
    This paper compares centrality indices usage within a heuristic method for a fast spread of alarm, or crucial information. Such indices can be used as a core part within more sophisticated optimisation methods, which should determine a graph parameter - burning number, defining, how fast can an alarm spread through all nodes. In this procedure at each time step a new chosen node is alarmed (i.e. burned) “from outside”, and already alarmed nodes at each time step then alarm their neighbours. The procedure ends, when all the nodes are alarmed (i.e. burned). The optimisation heuristic should choose such ordered sequence of nodes, which are to be alarmed “from outside”, that their number, equal the number of time steps (i.e. burning number) necessary to alarm the whole network, is minimised. The NP completeness of the problem necessitates a usage of heuristics. However, even the heuristics can be slower, reaching towards a global optimum, or faster, exchanging part of the quality for a time. This paper studies the usage of centrality indices in a simpler and faster heuristic. It should be useful e.g. for a mobile network of cars or drones, when an optimal solution cannot be computed in advance, or take too much CPU time, since the connections within the dynamic network might not exist any longer. A wide range of centrality indices was tested on selected networks, both real as well as artificially generated. While the performances of indices substantially differ for different types of networks, results show, which centrality indices work well across all tested networks.
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    An Ensemble-Based Malware Detection Model Using Minimum Feature Set
    (Institute of Automation and Computer Science, Brno University of Technology, 2019-12-20) Zelinka, Ivan; Amer, Eslam
    Current commercial antivirus detection engines still rely on signature-based methods. However, with the huge increase in the number of new malware, current detection methods become not suitable. In this paper, we introduce a malware detection model based on ensemble learning. The model is trained using the minimum number of signification features that are extracted from the file header. Evaluations show that the ensemble models slightly outperform individual classification models. Experimental evaluations show that our model can predict unseen malware with an accuracy rate of 0.998 and with a false positive rate of 0.002. The paper also includes a comparison between the performance of the proposed model and with different machine learning techniques. We are emphasizing the use of machine learning based approaches to replace conventional signature-based methods.