Abstract
Aiming at the problem of inaccurate analysis of forged address attack behavior in current detection methods, this paper proposes a large-scale network attack detection method based on machine learning. A large-scale network internal attack detection model is constructed to analyze the degree of network internal attack. The model is trained by machine learning method to improve the convergence speed of model detection. After determining the attack target, analyzing the network vulnerability attribute, tracing the network internal attack source. Computes the hash of a function to ensure that the data set is complete and not tampered with. Alarm windows automatically check the point, set different alarm levels. The Euclidean distance is used as the distance measure of clustering process to distinguish the attack behavior. The detection formula of large-scale network internal attack is constructed, and the calculation result is divided into the level of large-scale network internal attack. Experimental results show that the method is consistent with the actual path, and the highest recall is 93%, the highest precision is 0.98, with good detection results.
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Liu, C., Long, C., Yu, Y., Lin, Z. (2023). Research on Detection Method of Large-Scale Network Internal Attack Based on Machine Learning. In: Xu, Y., Yan, H., Teng, H., Cai, J., Li, J. (eds) Machine Learning for Cyber Security. ML4CS 2022. Lecture Notes in Computer Science, vol 13656. Springer, Cham. https://doi.org/10.1007/978-3-031-20099-1_7
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DOI: https://doi.org/10.1007/978-3-031-20099-1_7
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