RESEARCH ON THE EFFECTIVENESS OF ASSOCIATIVE RULES FOR EXTRACTING KNOWLEDGE FROM NETWORK TRAFFIC DATA FOR CYBERSECURITY PURPOSES

Sabinin Oleg Yurievich, Turusov Roman Andreevich, Chuprina Roman Vladimirovich

This paper investigates the effectiveness of using associative rules in analyzing network traffic data to discover new knowledge that can be useful for cybersecurity purposes. Associative rules allow the discovery of hidden dependencies and patterns in data, which can carry information missed by other machine learning techniques. The paper presents key approaches and algorithms for generating associative rules, their advantages and limitations. The results of an experimental study demonstrating the effectiveness of associative rules in analyzing network traffic are presented. The necessity of associative rules integration into traffic analysis systems is substantiated and directions for further research in this area are suggested.

Keywords:associative rules, knowledge extraction, traffic analysis, machine learning, data analysis, big data, network security, traffic optimization