Enhancing Wireless Sensor Network Security Through Mutual Information Analysis for Intrusion Detection and Resilience

Abstract

Intrusion detection is a critical aspect of network security, involving the identification and response to unauthorized access or malicious activities within a system. It plays a crucial role in safeguarding networks and ensuring data integrity and confidentiality. The Mutual Information (MI) analysis is a vital component in the realm of intrusion detection and security within Wireless Sensor Networks (WSNs). By assessing the information shared between pairs of sensor nodes, MI analysis reveals underlying patterns and relationships in network data. This process involves calculating the mutual information for each node pair based on statistical properties of observed values. Strong correlations or dependencies between nodes are identified, aiding in the detection of critical nodes or clusters vulnerable to compromise. Additionally, MI analysis informs feature extraction by guiding the selection of informative features that capture network structure. It also serves as a proactive tool for identifying anomalies or deviations from expected patterns, which may signal intrusion attempts or malicious activities. Through monitoring changes in mutual information over time, MI analysis facilitates prompt responses to emerging threats, enhancing the resilience and security of WSNs. The work exhibits high accuracy, recall rates, and detection rates across various attack scenarios, underscoring its efficacy in identifying and mitigating security threats. The algorithm's efficiency, effectiveness, and reliability make it a promising solution for enhancing WSN security. Through sophisticated methodologies and adaptive defensive strategies, the proposed algorithm strengthens the robustness and dependability of WSNs, minimizing the risk of potential security vulnerabilities and ensuring comprehensive threat detection in real-world deployment scenarios.