Enhancing Security in Wireless Sensor Networks with a Deep Learning and Game Theory-Based Hybrid Model for Malicious Node Detection

WSN are a type of self-organising wireless network that use a large number of inexpensive, low-volume sensor nodes set up in a multi-hop fashion. Applications such as scientific observation, node analysis, and environmental monitoring make extensive use of these networks. But when used in unprotected areas, they are open to security breaches, especially when injected with false data by malicious actors, which makes detection systems less reliable. The crucial problem of identifying malevolent nodes in WSNs is tackled in this research. Sensor nodes, cluster heads, and gateways are the three main components of a WSN design. Sensor nodes gather data, while cluster heads process it. During the dataset cleaning step, outlier detection was employed to increase data quality. Then, the KMCIG approach was used for feature extraction. An intriguing strategy for improving decision-making in complicated network settings is the study's integration of RL-GameTheory. The suggested approach detects malicious nodes more accurately and robustly by utilising this combination. The experimental findings show that the suggested model outperforms the current systems by a stunning margin, reaching an accuracy of 99.62%. This study demonstrates the promise of RL and game theory for creating cutting-edge security solutions for networks and helps move the field closer to intelligent, secure WSN frameworks.