Generative Adversarial Network for Detecting Cyber Threats in Industrial Systems

The transition from the information economy to the digital presents new challenges to the community related to the development of breakthrough technologies, a network of cyber-physical systems, artificial intelligence, and big data. When creating digital platforms, a number of difficulties arise: the large dimension of the digital infrastructure and its heterogeneity, poorly established information interaction between the segments, the lack of a common approach to ensuring cybersecurity, and high dependence on personnel qualification and reliability of equipment. The introduction of the digital economy leads to an increase in the risk of cyber threats associated with problems of access control between systems, regulation of information, and control flows. In this paper, for solving cyber threat detection tasks, it is proposed to use generative adversarial neural networks. The paper presents training and testing algorithms of the neural network. The result of the experiments demonstrated high accuracy at cyber threat detection.