Intrusion detection system based on improved abc algorithm with tabu search
Tianlong Gu, Hanyi Chen, Liang Chang, Long Li · IEEJ Transactions on Electrical and Electronic Engineering · 2019
An intrusion detection system (IDS) plays an important role in cyber security to detect network attacks. To improve the effectiveness of IDS, a new intrusion detection approach based on the support vector machine and improved Artificial Bee Colony algorithm (ABC) with Tabu Search (TS) is proposed. In the new method, to solve the problem of redundant network data and insufficient model parameters, a synchronous optimization strategy for feature selection and parameter calculation is proposed. At the same time, the idea of TS is a substitute for the greedy selection property of ABC, and two different selection probability formulas are provided in the early and later evolution of the ABC algorithm. The experimental results show that the newly proposed method performs better than other existing methods, especially in terms of detection accuracy and false negative rate. Besides, the detection rate of Probe attack and DoS attack reached 99.987 and 99.687%, respectively. © 2019 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.