Adaptive Defense Mechanisms Against Zero-Day Attacks in Wireless Sensor Networks

Important fields including healthcare, environmental monitoring, and military operations are seeing a rise in the use of wireless sensor networks (WSNs). But they are susceptible to complex cyber dangers, especially zero-day assaults, due to their dispersed nature and lack of resources. In order to protect WSNs against zero-day attacks, this research introduces an Adaptive Defense Mechanism (ADM). A dynamic response system that can adjust to changing attack patterns in real-time is integrated with machine learning-based anomaly detection in the proposed ADM. A hybrid model integrating Support Vector Machines (SVM) and Long Short-Term Memory (LSTM) networks is used by the anomaly detection module. It achieves a detection accuracy of 94.7% on simulated zero-day assault scenarios. In comparison to static defensive systems, the ADM's adaptive response mechanism, which is based on reinforcement learning, dynamically modifies the network's security regulations, resulting in a 23.5% reduction in false positives. Results from testing the proposed ADM in a simulated WSN setting show that it can greatly improve the network's ability to withstand zero-day assaults. With assaults present, the total network throughput increased by 15% and the average detection time dropped to 0.35 seconds. Based on these findings, the ADM provides a strong and effective defensive technique, guaranteeing the safety and dependability of WSNs in mission-critical applications.