Advanced Persistent Threats (APTs) Analysing: Current Detection Techniques and Emerging Countermeasures
Waqas Ahmed · Premier Journal of Artificial Intelligence · 2025
Advanced persistent threats (APTs) are a significant problem for organisational cyber security because of their sophistication and advanced attacks. This research article analyses the state of the art in current APT detection techniques and cutting-edge countermeasures. Different detection approaches, such as signature-based, behaviour-based, and AI-driven methods, are studied. The study points out the drawbacks of traditional signature-based detection and showcases the rising significance of behavioural analysis and machine learning for spotting sophisticated patterns of APT. Federated learning, blockchain technology for secure communication, deception techniques, and advanced forms of APT defence are explored. The article also assesses what tools and frameworks exist for APT detection, with an open-source versus proprietary comparison. We then discuss key APT detection and mitigation challenges, including detecting stealthy and polymorphic threats, data privacy problems, and resource constraints in real time. Future directions for the research are proposed, centred on explainable AI, collaborative defence mechanisms, and more effective detection in resource-constrained environments. This further advances the body of current work focused on developing more robust and adaptive security procedures against the tricky sophistication of cyber threats, proving beneficial to security experts and researchers attempting to strengthen organisational resiliency to APTs.