Intrusion Detection Method Based on Improved Sparrow Algorithm and Optimized SVM
Zechao Liu, Ran Shi, Mingyu Lei, Yulin Wu · 2022
With the rapid growth of network data traffic, Intrusion detection systems are more and more complicated in detecting attacks and anomalies. Support Vector Machine(SVM) is a major classifier that performs consistently well for the validity of IDS on network security defense. In this paper, we propose a new SVM intrusion detection method based on improved sparrow optimization algorithm. It aims to solve the problem that sparrow search algorithm (SSA) lacks in the ability of jumping from local optimization solution. The simulation results on KDD99 data set show that our proposed method has better effects than other methods.