A comparative analysis of CGAN-based oversampling for anomaly detection

IF 1.7 Q3 COMPUTER SCIENCE, INFORMATION SYSTEMS
Rahbar Ahsan, Wei Shi, Xiangyu Ma, William Lee Croft
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引用次数: 8

Abstract

In this work, the problem of anomaly detection in imbalanced datasets, framed in the context of network intrusion detection is studied. A novel anomaly detection solution that takes both data-level and algorithm-level approaches into account to cope with the class-imbalance problem is proposed. This solution integrates the auto-learning ability of Reinforcement Learning with the oversampling ability of a Conditional Generative Adversarial Network (CGAN). To further investigate the potential of a CGAN, in imbalanced classification tasks, the effect of CGAN-based oversampling on the following classifiers is examined: Naïve Bayes, Multilayer Perceptron, Random Forest and Logistic Regression. Through the experimental results, the authors demonstrate improved performance from the proposed approach, and from CGAN-based oversampling in general, over other oversampling techniques such as Synthetic Minority Oversampling Technique and Adaptive Synthetic.

Abstract Image

基于cgan的过采样异常检测的比较分析
本文在网络入侵检测的背景下,研究了不平衡数据集的异常检测问题。提出了一种同时考虑数据级和算法级方法的异常检测方法。该解决方案将强化学习的自动学习能力与条件生成对抗网络(CGAN)的过采样能力相结合。为了进一步研究CGAN的潜力,在不平衡分类任务中,研究了基于CGAN的过采样对以下分类器的影响:Naïve贝叶斯、多层感知器、随机森林和逻辑回归。通过实验结果,作者证明了该方法的性能优于其他过采样技术,如合成少数派过采样技术和自适应合成过采样技术。
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来源期刊
IET Cyber-Physical Systems: Theory and Applications
IET Cyber-Physical Systems: Theory and Applications Computer Science-Computer Networks and Communications
CiteScore
5.40
自引率
6.70%
发文量
17
审稿时长
19 weeks
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