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An Improved Random Forest Intrusion Detection Model Based on Tent Mapping

At present, there are a lot of algorithms about Intrusion Detection System (IDS) of the Wireless Sensor Network (WSN). However, based on the complexity of the environment and its own characteristics, the traditional intrusion detection technology has some problems, such as low detection rate and slow detection rate for different kinds of intruders. In order to enhance the accuracy of the model, this paper introduces Random Forest (RF) and Arithmetic Optimization Algorithm (AOA) to solve the intrusion detection problem when WSN receives DDoS attack, with higher accuracy and lower error rate. The improved tent chaotic map is used to increase the diversity of individuals; The parallel strategy enhances the communication between populations and adjusts the optimization. Firstly, the PT -AOA algorithm proposed has excellent performance in the evaluation of test function, and effectively ensures the improvement of RF classifier. On this basis, the optimized RF intrusion detection model has better performance than the traditional machine learning method in the simulation experiments on WSN-DS and CICDDoS2019 data sets.