基于生成对抗网络的入侵检测类别不平衡问题数据增强方法  

Data Augmentation Method for Intrusion Detection Imbalance Problem Using Generative Adversarial Networks

作　　者：孙佳佳 李承礼 常德显[1] 高立伟 SUN Jia-jia;LI Cheng-li;CHANG De-xian;GAO Li-wei(PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China)

机构地区：[1]战略支援部队信息工程大学,郑州450001

出　　处：《科学技术与工程》2022年第18期7965-7971,共7页Science Technology and Engineering

基　　金：国家社会科学基金(21BXW057);国家自然科学基金(61902427);河南省科技公共项目(212102210162)。

摘　　要：数据类别不平衡问题是制约机器学习技术在入侵检测领域应用效果的重要因素。当训练数据不均衡时,训练得到模型的分类结果往往倾向多数类,从而极大影响分类效果。针对基于机器学习算法进行入侵检测时训练样本不均衡以及由于数据隐私性导致训练样本不足和更新慢的问题,提出一种基于生成对抗网络和深度神经网络相结合的入侵数据增强方法,以实现样本集的类别均衡。通过NSL-KDD数据集对模型评估,本文所提方法不仅具有较高的准确率,而且对未知攻击和只有少数样本的攻击类型具有较高的检测率。Class imbalance problem is a key factor that restricts the application of machine learning technology in the field of intrusion detection.The classification result will be significantly affected if the training data is imbalanced due to the classifier’s tendency towards the majority class.A data augmentation method for intrusion detection combining generative adversarial networks and deep neural network was proposed in order to solve the problem of sample imbalance and lack of sample because of the confidentiality of the data.Finally,the model was evaluated on the NSL-KDD dataset and the results show that our method not only has higher accuracy rate,but also has higher detection rate for unknown attacks and attack types with only a few samples compared with the traditional algorithms.

关 键 词：生成对抗网络 入侵检测 非均衡数据集 数据增强 

分 类 号：TP391.41[自动化与计算机技术—计算机应用技术]