Detection of Anomalies in the Information Networks of Industrial Automation Systems Based on Artificial Immune Detectors

The actions of attackers are the main sources of anomalies in the information networks of industrial automation systems, which can lead to serious consequences up to the occurrence of emergency situations. Neutralizing software development, as well as the development of new network anomalous behavior detection methods is one of the most important tasks. This article proposes a heuristic anomalies detection method based on the use of immune detectors in the form of the Kohonen layer and the hidden Markov model. Conclusions contain the results obtained in comparison with other anomaly detection systems. This paper proves that the proposed method can serve as a basis for building effective anomalies detection systems.