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Heuristic Intrusion Detection Based on Traffic Flow Statistical Analysis

Author

Listed:
  • Wojciech Szczepanik

    (Department of Telecommunications, AGH University of Science and Technology, Mickiewicza 30, 30-059 Krakow, Poland
    These authors contributed equally to this work.)

  • Marcin Niemiec

    (Department of Telecommunications, AGH University of Science and Technology, Mickiewicza 30, 30-059 Krakow, Poland
    These authors contributed equally to this work.)

Abstract

As telecommunications are becoming increasingly important for modern systems, ensuring secure data transmission is getting more and more critical. Specialised numerous devices that form smart grids are a potential attack vector and therefore is a challenge for cybersecurity. It requires the continuous development of methods to counteract this risk. This paper presents a heuristic approach to detecting threats in network traffic using statistical analysis of packet flows. The important advantage of this method is ability of intrusion detection also in encrypted transmissions. Flow information is processing by neural networks to detect malicious traffic. The architectures of subsequent versions of the artificial neural networks were generated based on the results obtained by previous iterations by searching the hyperparameter space, resulting in more refined models. Finally, the networks prepared in this way exhibited high performance while maintaining a small size—thereby making them an effective method of attacks detection in network environment to protect smart grids.

Suggested Citation

  • Wojciech Szczepanik & Marcin Niemiec, 2022. "Heuristic Intrusion Detection Based on Traffic Flow Statistical Analysis," Energies, MDPI, vol. 15(11), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:11:p:3951-:d:825349
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    References listed on IDEAS

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    1. Kamran Shaukat & Suhuai Luo & Vijay Varadharajan & Ibrahim A. Hameed & Shan Chen & Dongxi Liu & Jiaming Li, 2020. "Performance Comparison and Current Challenges of Using Machine Learning Techniques in Cybersecurity," Energies, MDPI, vol. 13(10), pages 1-27, May.
    2. Milosz Smolarczyk & Sebastian Plamowski & Jakub Pawluk & Krzysztof Szczypiorski, 2022. "Anomaly Detection in Cyclic Communication in OT Protocols," Energies, MDPI, vol. 15(4), pages 1-20, February.
    3. Mohit Mittal & Rocío Pérez de Prado & Yukiko Kawai & Shinsuke Nakajima & José E. Muñoz-Expósito, 2021. "Machine Learning Techniques for Energy Efficiency and Anomaly Detection in Hybrid Wireless Sensor Networks," Energies, MDPI, vol. 14(11), pages 1-21, May.
    4. Shahid Tufail & Imtiaz Parvez & Shanzeh Batool & Arif Sarwat, 2021. "A Survey on Cybersecurity Challenges, Detection, and Mitigation Techniques for the Smart Grid," Energies, MDPI, vol. 14(18), pages 1-22, September.
    5. Youba Nait Belaid & Patrick Coudray & José Sanchez-Torres & Yi-Ping Fang & Zhiguo Zeng & Anne Barros, 2021. "Resilience Quantification of Smart Distribution Networks—A Bird’s Eye View Perspective," Energies, MDPI, vol. 14(10), pages 1-29, May.
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    Cited by:

    1. Akash Kumar & Bing Yan & Ace Bilton, 2022. "Machine Learning-Based Load Forecasting for Nanogrid Peak Load Cost Reduction," Energies, MDPI, vol. 15(18), pages 1-23, September.

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