Harnessing Machine Learning for APTs Detection and Mitigation in Large-Scale Networks

A significant and ongoing cyber security concern is the presence of Advanced Persistent Threats (APTs), especially in large-scale networks. APTs are known for their lengthy lifespan, stealth, and sophistication. As a result, they frequently elude conventional security measures, calling for more flexible and durable solutions. Machine learning (ML) has become a potent instrument for improving these sophisticated threats’ detection and response. This review paper investigates the use of machine learning (ML) to counteract advanced persistent threats (APTs), emphasizing critical techniques including anomaly detection, behavioural analysis, and threat intelligence integration. We investigate how successfully different machine learning algorithms-such as deep learning models, supervised, unsupervised, and reinforcement learning approaches-identify and react to APT activities. The paper also discusses important obstacles to overcome when using machine learning (ML) for APT detection, such as problems with data quantity and quality, attacker evasion strategies, scalability issues, and more. We highlight the advantages and disadvantages of existing MLbased techniques in practice using real-world examples. The report also addresses future areas for research, highlighting the necessity of improved model robustness, adaptive learning capabilities, and cooperative efforts to remain ahead of developing cyber threats. With an emphasis on both the state of the art and potential directions for future research, this review seeks to give readers a thorough grasp of how machine learning (ML) can be used to fortify cyber security defences against advanced persistent threats (APTs) in large-scale network systems.