Fooling intrusion detection systems using adversarially autoencoder  

作　　者：Junjun Chen Di Wu Ying Zhao Nabin Sharma Michael Blumenstein Shui Yu 

机构地区：[1]College of Information Science and Technology,Beijing University of Chemical Technology,Beijing,100029,China [2]School of Computer Science,University of Technology Sydney,Ultimo,2007,Australia [3]Centre for Artificial Intelligence,University of Technology Sydney,Ultimo,2007,Australia

出　　处：《Digital Communications and Networks》2021年第3期453-460,共8页数字通信与网络（英文版）

摘　　要：Due to the increasing cyber-attacks,various Intrusion Detection Systems(IDSs)have been proposed to identify network anomalies.Most existing machine learning-based IDSs learn patterns from the features extracted from network traffic flows,and the deep learning-based approaches can learn data distribution features from the raw data to differentiate normal and anomalous network flows.Although having been used in the real world widely,the above methods are vulnerable to some types of attacks.In this paper,we propose a novel attack framework,Anti-Intrusion Detection AutoEncoder(AIDAE),to generate features to disable the IDS.In the proposed framework,an encoder transforms features into a latent space,and multiple decoders reconstruct the continuous and discrete features,respectively.Additionally,a generative adversarial network is used to learn the flexible prior distribution of the latent space.The correlation between continuous and discrete features can be kept by using the proposed training scheme.Experiments conducted on NSL-KDD,UNSW-NB15,and CICIDS2017 datasets show that the generated features indeed degrade the detection performance of existing IDSs dramatically.

关 键 词：Intrusion detection Cyber attacks Autoencoder Generative adversarial networks 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]