Adaptive Honeypot Architectures for Detecting Advanced Persistent Threats (APTs)
DOI:
https://doi.org/10.15662/IJEETR.2021.0304002Keywords:
Adaptive Honeypot, Advanced Persistent Threats (APTs), Deception Techniques, Reinforcement Learning, Behavioral Profiling, Anomaly Detection, Cybersecurity, Intrusion Detection SystemsAbstract
Advanced Persistent Threats (APTs) represent a significant challenge to cybersecurity due to their stealthy, targeted, and prolonged nature. Traditional defense mechanisms often struggle to detect these sophisticated attacks. Adaptive honeypot architectures have emerged as a promising solution, leveraging deception techniques to lure and analyze attackers. These systems dynamically adjust their behavior based on real-time interactions, enhancing the detection and understanding of APTs.
This paper explores the evolution, design, and effectiveness of adaptive honeypot architectures in detecting APTs. We examine various approaches, including reinforcement learning-based systems like Heliza and QRASSH, which adapt their responses to attacker behavior. Additionally, we discuss the integration of machine learning techniques for behavioral profiling and anomaly detection, exemplified by systems such as Honeyboost and HoneyIoT.
The proposed architectures offer several advantages, including improved detection rates, reduced false positives, and enhanced understanding of attacker tactics. However, challenges remain, such as the risk of honeypot detection by adversaries and the complexity of implementation. Through a comprehensive analysis, this paper provides insights into the current state and future directions of adaptive honeypot systems in the context of APT detection.
References
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2. Chacon, J., McKeown, S., & Macfarlane, R. (2020). Towards identifying human actions, intent, and severity of APT attacks applying deception techniques.
