Malware Detection Algorithm for Wireless Sensor Networks in a Smart City Based on Random Forest

Abstract

Abstract Aiming at the problems of inaccurate malware detection in traditional wireless sensor network detection algorithms, resulting in inaccurate prediction of network residual energy and low network life, a malware detection algorithm for wireless sensor networks based on random forest is proposed. Firstly, the random forest is optimized and introduced into software detection. Based on this, the attack model and software trust of malware are calculated to realize the detection of malware in wireless sensor networks. The experimental results show that the proposed algorithm can effectively improve the detection rate, and the prediction of network residual energy is accurate, which can effectively prolong the service life of the network.