Heuristic Intrusion Detection Based on Traffic Flow Statistical Analysis

Wojciech Szczepanik and Marcin Niemiec
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Wojciech Szczepanik: Department of Telecommunications, AGH University of Science and Technology, Mickiewicza 30, 30-059 Krakow, Poland
Marcin Niemiec: Department of Telecommunications, AGH University of Science and Technology, Mickiewicza 30, 30-059 Krakow, Poland

Energies, 2022, vol. 15, issue 11, 1-19

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.

Keywords: cybersecurity; intrusion detection; network attacks; machine learning; artificial neural networks; smart grids (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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