Abstract
Cyber-intrusions are constantly growing due to the ineffectiveness of the traditional cyber security tools and filtering systems-based attacks detection. In the last decade, significant techniques of machine and deep learning were employed to resolve the cyber security issues. Unfortunately, the results are still imprecise with a lot of shortcomings. In this paper, we present a real-time cyber security agent based on honeypots technology for real-time data collection and a combination of machine learning algorithms for data modeling that enhances modeling accuracy.
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El Kamel, N., Eddabbah, M., Lmoumen, Y., Touahni, R. (2022). A Real-Time Smart Agent for Network Traffic Profiling and Intrusion Detection Based on Combined Machine Learning Algorithms. In: Ben Ahmed, M., Teodorescu, HN.L., Mazri, T., Subashini, P., Boudhir, A.A. (eds) Networking, Intelligent Systems and Security. Smart Innovation, Systems and Technologies, vol 237. Springer, Singapore. https://doi.org/10.1007/978-981-16-3637-0_21
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DOI: https://doi.org/10.1007/978-981-16-3637-0_21
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