Intrusion Detection in Wireless Sensor Networks Based on IPSO-SVM Algorithm

To optimize node energy consumption and improve its security, this paper uses the DEEC algorithm to layer WSN and reduce the probability of channel information collision and uses the weighted probability of cluster head election to optimize node energy expenditure, so that WSN can obtain a longer lifecycle. Improved Particle Swarm Optimization-based Support Vector Machine (IPSO-SVM) algorithm is used for intrusion detection and experimental testing in WSN. The results showed that the IPSO-SVM algorithm exhibited good convergence, with a convergence step size of 5 steps, which converged earlier than the Support Vector Machine Algorithm based on Particle Swarm Optimization (PSO-SVM), which had a convergence step size of 10 steps. The IPSO-SVM algorithm performed best in WSN intrusion detection, with the highest detection rate of 96.20% in Probe attack data detection, which was 0.80% higher than the Support Vector Machine Algorithm based on Genetic Algorithm (GA-SVM). The PSO-SVM algorithm had the lowest detection rate of 95.20%. The IPSO-SVM algorithm had a minimum false positive rate of 1.54% in Dos attack data detection. In terms of average training time, the IPSO-SVM algorithm had a minimum average training time of 323.45 seconds. Compared to the Low Energy Adaptive Clustering Hierarchy (LEACH) algorithm, the Distributed Energy Efficient Clustering (DEEC) algorithm performs better, has less energy consumption, and retains more nodes. The method adopted in this study can make WSN have a longer life cycle and ensure its security.