Relevance of the research topic. Modern cyber attacks are becoming more complex and diverse, which makes classical methods of detecting anomalies, such as signature and heuristic, insufficiently effective. In this regard, it is necessary to develop more advanced systems for detecting network threats based on machine learning and artificial intelligence technologies. Problem statement. Existing methods of detecting malicious traffic often face problems associated with high false-positive response and insufficient accuracy in the face of real threats on the network. This reduces the effectiveness of cybersecurity systems and makes it difficult to identify new attacks. The purpose of the study. The purpose of this work is to develop a malicious traffic detection system that would increase the number of detected anomalies in network traffic through the introduction of machine learning and AI technologies. Research methods. To achieve this goal, a thorough analysis and preprocessing of data obtained from publicly available datasets such as CICIDS2017 and KDD Cup 1999 was carried out.

Keywords: anomaly detection, malicious traffic, cybersecurity, machine learning, artificial intelligence, signature methods