METHODS AND MODELS OF COUNTERING GROUP CYBER THREATS BASED ON ARTIFICIAL INTELLIGENCE

Abstract

The article addresses the pressing issue of countering group threats in the field of cybersecurity, which are characterized by a high level of organization, complex structures, and targeted execution. With the advancement of digital technologies and the growing dependency of businesses and government institutions on information systems, threats carried out by coordinated groups-such as APTs, botnets, and other cybercriminal organizations-pose an increasing danger. Traditional security approaches have proven ineffective against such attacks, as they often fail to consider the dynamic behavior of threats, their rapid evolution, and adaptability to defensive mechanisms. A method for countering group threats based on artificial intelligence technologies is proposed, specifically leveraging machine learning, deep learning, and big data processing techniques. The developed model architecture enables the detection of signs of coordinated malicious activity, analysis of attacker behavioral patterns, and timely response to potential threats. Special attention is given to the development of adaptive models capable of real-time self-learning and identifying atypical deviations from the normal functioning of an information system. The models are designed with key characteristics of group attacks in mind, including scenarios of distributed attacks, hidden communication between attackers, and the use of encryption and evasion techniques. Experimental studies on the effectiveness of the proposed approach were conducted using test datasets and realistic threat scenarios. The results demonstrated high detection accuracy, a reduction in false positives, and improved response times compared to traditional systems. The findings confirm the feasibility and effectiveness of using intelligent methods to counter complex group threats. The proposed method and models can be integrated into existing cybersecurity monitoring systems to enhance their resilience against modern attacks. The article also outlines prospects for further research, particularly in improving model training methods and scaling to large corporate networks.