Detection of Network Attacks in a Heterogeneous Industrial Network Based on Machine Learning
Alexey Vulfin · Programming and Computer Software · 2023
Abstract Issues of improving algorithms for detecting network attacks in a heterogeneous industrial Internet of Things network based on machine learning technologies for subsequent integration with subsystems of a security operation center are considered. A block diagram of a network attack detection system and an algorithm for the intelligent analysis of network traffic parameters in the task of detecting malicious network activity are developed. Variants of constructing ensembles of classifiers based on machine learning models and heterogeneous neural network models are analyzed. The F1 score for test samples from publicly available datasets of labeled network traffic is as high as 96%. The possibility of embedding the proposed models into software and hardware modules is discussed. A virtual testbed for assessing the effectiveness of machine learning models for detecting network attacks is developed.