Artificial Intelligence and Machine Learning Algorithms for Cyber Attack Countermeasures: A Comprehensive Literature Review

Abstract: The exponential growth of cyber threats in the digital era has necessitated the development of sophisticated countermeasures that can adapt to evolving attack vectors. This comprehensive literature review examines the application of artificial intelligence (AI) and machine learning (ML) algorithms in developing effective cyber attack countermeasures from 2000 to 2024. Through systematic analysis of 142 peer-reviewed publications, this study identifies key AI/ML techniques including deep neural networks, ensemble methods, reinforcement learning, and hybrid approaches that have demonstrated significant efficacy in threat detection, prevention, and response. The research reveals that while traditional signature-based security systems achieve detection rates of 60-75%, AI-driven solutions consistently demonstrate superior performance with accuracy rates exceeding 95% in controlled environments. However, challenges persist in areas such as adversarial attacks, model interpretability, and real-time deployment constraints. This review synthesizes current methodologies, evaluates their effectiveness across different attack scenarios, and provides insights into future research directions for AI- enhanced cybersecurity frameworks.

Keywords: Artificial Intelligence, Machine Learning, Cybersecurity, Intrusion Detection, Threat Intelligence, Deep Learning, Malware Detection