Application of Information Theoretical Principles for Detection and Prevention of Waterhole Attacks: An Investigative Study

Abstract

Waterhole attacks have emerged as a stealthy and effective cyber threat, targeting specific groups by compromising trusted websites. Traditional detection mechanisms often fail to identify these attacks due to their low visibility and reliance on legitimate sites. This study investigates the application of information theoretical principles including entropy, mutual information, and divergence metrics to detect and prevent waterhole attacks. By measuring randomness, quantifying correlation, and identifying distribution drifts, information-theoretic approaches offer robust anomaly detection capabilities. The research highlights how entropy-based detection can identify irregular traffic patterns, while mutual information reveals deviations in web traffic relationships, signaling potential malicious activity. Furthermore, Kullback-Leibler Divergence (KLD) effectively captures distribution changes in network behavior, aiding in early detection. The study also explores real-world case studies and evaluates the performance of information-theoretic methods, demonstrating their accuracy and resilience in identifying stealthy attack patterns. The findings suggest that applying information theory enhances the detection of low-profile, targeted attacks by distinguishing between legitimate and anomalous activities. This study underscores the potential of adaptive, information-theoretic defense mechanisms to improve cybersecurity resilience against waterhole attacks, making them a promising solution for advanced intrusion detection systems (IDS) and threat prevention frameworks.