With the rapid development of wireless sensor technology, the application of Wireless Sensor Networks (WSNs) is more and more extensive, and has important military value and broad commercial application prospect. However, due to the limited resources of terminal equipment, wireless communication environment and other reasons, it faces severe security problems. This paper mainly proposes an intrusion detection algorithm based on improved AdaBoost-RBFSVM, and designs an intrusion detection system (IDS) for WSNs denial of service (DoS) attack based on the proposed method. In order to make the RBF-SVM algorithm as the AdaBoost weak classifier, the effect of training is achieved. Using the influence of parameter σ to RBF-SVM and the effect of model training error em on the smoothness of AdaBoost weights, the IABRBFSVM algorithm is proposed. On the other hand, after analyzing the DoS attack, the eigenspace for the attack is proposed, and the corresponding intrusion detection system is designed. Through simulation, the proposed IDS can significantly improve the network performance by detecting and removing malicious nodes in the network, from the perspective of detection rate, packet delivery rate, transmission delay and energy consumption analysis, and has the characteristics of simple structure, short computation time and high detection rate.
Jianjian, D., Yang, T., & Feiyue, Y. (2018). A novel intrusion detection system based on IABRBFSVM for wireless sensor networks. In Procedia Computer Science (Vol. 131, pp. 1113–1121). Elsevier B.V. https://doi.org/10.1016/j.procs.2018.04.275