Abstract
Recent approaches based on feature transformation to images with further application of the pre-trained deep analysis models have been adopted for different cyber security tasks such as malware detection, intrusion and anomaly detection. The transfer learning (TL) is a technique for solving a new task using experience or knowledge transfer from a solution of the related task. Such approach allows speeding up the process of the problem solution or increasing its performance. This paper reviews existing approaches based on transfer learning with particular focus on data preprocessing step, discusses their advantages and disadvantages. The paper ends up with the proposed approach to feature extraction based on the traffic packets transformation to images, and evaluates its efficiency using Secure Water Treatment data set (SWaT) that models functioning of the modern water treatment facility.
This research is being supported by the grant of RSF #22-21-00724 in St. Petersburg Federal Research Center of the Russian Academy of Sciences.
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Golubev, S., Novikova, E. (2023). Image-Based Intrusion Detection in Network Traffic. In: Braubach, L., Jander, K., Bădică, C. (eds) Intelligent Distributed Computing XV. IDC 2022. Studies in Computational Intelligence, vol 1089. Springer, Cham. https://doi.org/10.1007/978-3-031-29104-3_6
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