Intrusion detection method based on improved social network search algorithm

Abstract

The network security problem in today's world is becoming more and more prominent, and intrusion detection as a branch in the field of network security has been developed tremendously. At present, back propagation (BP) neural network is widely used in intrusion detection. However, its weights and thresholds are randomly initialized, so that fall into local optimal after training. To solve this problem, an intrusion detection model based on improved social network search (ISNS) algorithm to optimize BP neural network is proposed. The model first uses chaotic mapping and elite mechanism to improve the original Social Network Search (SNS) algorithm, and then uses the ISNS algorithm to filter the initial weights and thresholds of the BP neural network, and constructs the neural network with the optimal values to avoid falling into local optimum. The experimental results show that the proposed method solves the problem that the traditional BP neural network is easy to fall into local optimum. At the same time, the ISNS_BP model achieves better classification performance than other models, and the accuracy on the NSL-KDD and UNSW-NB15 datasets reaches 98.62% and 93.97%, respectively.