A hybrid method for modeling artificial intelligence systems to detect cyberattacks

Abstract

Objective. The aim of the research is to develop an adaptive system for protecting critical life support infrastructure based on hybrid AI methods that combine machine learning (ML) and reinforcement learning. Method. A hybrid method of modeling artificial intelligence systems to detect cyber attacks is used. The basis of the method is a combination of reinforcement learning and anomaly analysis, which allows the system to automatically adapt to new threats. Result. It is proposed to implement artificial intelligence systems at the stages of monitoring, analysis of the received data and prompt response to eliminate the threat. The system includes the development of hybrid models for data analysis, which combines information from external sources and event logs. The use of the system will increase the stability of the infrastructure, reduce vulnerability to threats, and ensure uninterrupted operation in the conditions of an information threat. Conclusion. New approaches to the use of artificial intelligence systems are considered. Artificial intelligence models based on machine learning are proposed, allowing for the detection of not only old threats but also atypical scenarios of information hacks in a short time. Predictive algorithms are used to analyze the abnormal behavior of the malicious system, and deep learning provides accurate conclusions about threat classification.