Literature review on advanced persistent threats management with deception techniques

This study investigates the dynamic threat landscape of cybersecurity, with a particular focus on Advanced Persistent Threats (APTs) and various web-based and network-based attacks. Through a theoretical approach, it examines key attack vectors including structural query language (SQL) Injection, Cross-Site Scripting (XSS), Cross-Site Request Forgery (CSRF), Directory Traversal, Server-Side Request Forgery (SSRF), and Command Injection, highlighting the mechanisms through which attackers exploit vulnerabilities in web and network systems. To counter these evolving threats, the research explores theoretical frameworks such as Game Theory and Reinforcement Learning (RL). Game Theory is applied to honeypot optimisation, modelling strategic interactions between attackers and defenders, while RL enables adaptive learning for dynamic defence configurations. The integration of these concepts presents a proactive cybersecurity approach, improving detection capabilities, resource allocation, and system resilience. This study concludes that combining strategic modelling with intelligent learning systems is vital for building robust cybersecurity defences capable of addressing modern and emerging threats.