关键词: 入侵检测, 生成式对抗网络, 极限学习机, 粒子群算法, 支持向量机

Abstract:

Aiming at the problem of low detection rate of a few classes of machine learning methods in dealing with unbalanced massive intrusion data, this paper proposes an intrusion detection method（GAN-PSO-ELM） which combines the Generative Adversarial Nets（GAN）, Particle Swarm Optimiztion（PSO） and Extreme Learning Machine（ELM）. The original network data is preprocessed, and the data set with a few kinds of samples are expanded by using Gan and the way of whole class expansion. On the extended balance data set, PSO is used to optimize the input weights and hidden layer thresholds of elm, and an intrusion detection model is established. The simulation experiments are carried out on NSL-KDD data set. The experimental results show that compared with SVM, ELM and PSO-ELM, GAN-PSO-ELM not only has a higher detection efficiency, but also has an average increase of 3.74% in the overall detection accuracy, 28.13% and 16.84% in a few R2L and U2R, respectively.

Key words: intrusion detection, Generative Adversarial Networks（GAN）, Extreme Learning Machine（ELM）, Particle Swarm Optimization（PSO）, Support Vector Machine（SVM）