Research and classification of the main types of attacks on artificial intelligence systems in cybersecurity

Abstract

The modern development of artificial intelligence (AI) and machine learning (ML) opens up new opportunities in the field of cybersecurity, but at the same time creates serious challenges in the form of intelligent cyberattacks. The study is devoted to the analysis and classification of ways to use AI for malicious purposes and the study of effective methods to counter such threats. In particular, the article covers the main types of attacks using ML technologies, which demonstrate how attackers can manipulate machine learning algorithms, undermine trust in data, and bypass protection systems. Special attention is paid to the mechanisms of data poisoning attacks, as they are considered the most influential in machine learning, which involve introducing malicious data into the process of training models, which leads to distortion of results and undermines the effectiveness of security algorithms. Evasion attacks are also considered, in which attackers create unique data samples that can remain invisible to traditional threat detection systems. Privacy attacks are analyzed as a way to obtain confidential information from ML models, which can be used to steal user data. Abuse attacks demonstrate how attackers can use AI tools to automate attacks, scale phishing campaigns, and analyze vulnerabilities in defense systems. The relevance of the study is due to the fact that traditional approaches to cyber defense are no longer able to effectively counter threats that adapt and evolve due to machine learning. The article emphasizes the critical importance of researching defense methods, in particular, building reliable machine learning systems that have built-in mechanisms for detecting anomalies and adapting to new threats. One of the key approaches is federated learning, which allows training models without centralized data storage, reducing the risk of information leakage. The development of deep learning in the field of cyber defense is also considered, which allows analyzing behavioral patterns of threats in real time. The combination of technological measures with human control remains an important aspect, since, despite the power of AI tools, the human factor remains key in the process of ensuring cybersecurity. Thus, the article demonstrates the balance between the opportunities and threats of AI in the field of cybersecurity, emphasizing the need for further research in the direction of resilient ML models that can effectively resist attacks. Without proper regulation and control, AI can become not only a defender, but also a tool for attackers, which requires the development of new security strategies and international regulation in the field of cybersecurity.