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Intelligent Algorithms for Event Processing and Decision Making on Information Protection Strategies against Cyberattacks

Author

Listed:
  • Grigorii Asyaev

    (Department of Information Security, South Ural State University, Chelyabinsk 454080, Russia)

  • Alexander Sokolov

    (Department of Information Security, South Ural State University, Chelyabinsk 454080, Russia)

  • Alexey Ruchay

    (Department of Information Security, South Ural State University, Chelyabinsk 454080, Russia
    Department of Mathematics, Chelyabinsk State University, Chelyabinsk 454001, Russia)

Abstract

This paper considers the main approaches to building algorithms for the decision support systems of information protection strategies against cyberattacks in the networks of automated process control systems (the so-called recommender systems). The advantages and disadvantages of each of the considered algorithms are revealed, and their applicability to the processing of the information security events of the UNSW-NB 15 dataset is analyzed. The dataset used contains raw network packets collected using the IXIA PerfectStorm software in the CyberRange laboratory of the Australian Cyber Security Centre (Canberra) in order to create a hybrid of the simulation of the real actions and the synthetic behavior of the network traffic generated during attacks. The possibility of applying four semantic proximity algorithms to partition process the data into clusters based on attack type in a distribution control system (DCS) is analyzed. The percentage of homogeneous records belonging to a particular type of attack is used as the metric that determines the optimal method of cluster partitioning. This metric was chosen under the assumption that cyberattacks located “closer” to each other in the multidimensional space have similar defense strategies. A hypothesis is formulated about the possibility of transferring knowledge about attacks from the vector feature space into a semantic form using semantic proximity methods. The percentage of homogeneous entries was maximal when the cosine proximity measure was used, which confirmed the hypothesis about the possibility of applying the corresponding algorithm in the recommender system.

Suggested Citation

  • Grigorii Asyaev & Alexander Sokolov & Alexey Ruchay, 2023. "Intelligent Algorithms for Event Processing and Decision Making on Information Protection Strategies against Cyberattacks," Mathematics, MDPI, vol. 11(18), pages 1-17, September.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:18:p:3939-:d:1241321
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    References listed on IDEAS

    as
    1. Tehseen Mazhar & Hafiz Muhammad Irfan & Sunawar Khan & Inayatul Haq & Inam Ullah & Muhammad Iqbal & Habib Hamam, 2023. "Analysis of Cyber Security Attacks and Its Solutions for the Smart grid Using Machine Learning and Blockchain Methods," Future Internet, MDPI, vol. 15(2), pages 1-37, February.
    2. Ali Alzahrani & Theyazn H. H. Aldhyani, 2023. "Design of Efficient Based Artificial Intelligence Approaches for Sustainable of Cyber Security in Smart Industrial Control System," Sustainability, MDPI, vol. 15(10), pages 1-29, May.
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