Network intrusion detection method based on IEHO-SVM

Abstract

As the growth of network technology, the network intrusion has become increasingly serious. An elephant herding optimization algorithm and support vector machine-based network intrusion detection method are proposed to address the difficulties and low detection accuracy of the detection. This method first uses an optimized elephant swarm optimization algorithm to select features from the intrusion data and then uses the elephant swarm optimization method to optimize the parameters of the support vector machine algorithm. Finally, a detection model is constructed based on support vector machines. The main contribution of the research is the proposal of a network intrusion detection method based on improved swarm optimization algorithm and support vector machine. By using an improved swarm optimization algorithm to optimize the parameters of the support vector machine classification algorithm, this method significantly improves the accuracy and stability of detection when dealing with the classification task of network intrusion detection. The experimental results show that the detection model has a stable average accuracy of around 94% in detecting four types of intrusion data, surpassing the performance of other commonly used algorithms. The results validate the effectiveness of introducing the improved elephant swarm optimization algorithm and demonstrate its superiority in intrusion detection tasks.