Multi-strategy improved tuna swarm optimization algorithm for feature selection of network intrusion detection
Zhengbin Qin, Hui Zhi Xu, Jin Yang, Lang Huang · 2023
As for current network intrusion detection technology, it has become critical to improve the problems of low detection efficiency and low accuracy caused by feature redundancy. To improve this problem, we propose a multi-strategy Improved Tuna Swarm Optimization (ITSO) algorithm and apply it to feature selection for network intrusion detection. First, an elite backward learning strategy is used to initialize the population and improve the quality of the solution in the initial stage to lay the foundation for the global optimum. Then, an improved sine cosine strategy is added to further optimize and balance the ability of local exploitation and global search before the tuna swarm starts feeding. Finally, the Levy flight-Cauchy variation strategy was added to the spiral foraging and parabolic foraging behavior of tuna schools to increase the diversity of group locations and improve the global search capability of the algorithm in network intrusion detection feature selection. The ITSO algorithm was tested against the original TSO algorithm and other classical algorithms in six benchmark functions. In terms of accuracy, it improved by 2.95% on the UNSW-NB15 dataset and 4.1% on the NSL-KDD dataset compared to the original TSO algorithm. Intrusion detection time is also greatly reduced. This effectively improves the performance of network intrusion detection.