Industrial Control Malicious Traffic Anomaly Detection System Based on Deep Autoencoder

Author:

Wang Weiping,Wang Chunyang,Guo Yongzhen,Yuan Manman,Luo Xiong,Gao Yang

Abstract

Industrial control network is a direct interface between information system and physical control process. Due to the lack of authentication, encryption, and other necessary security protection designs, it has become the main target of malicious attacks under the trend of increasing openness. In order to protect the industrial control systems, we examine the detection of abnormal traffic in industrial control network and propose a method of detecting abnormal traffic in industrial control network based on autoencoder technology. What is more, a new deep autoencoder model was designed to reduce the dimensionality of traffic data in industrial control network. In this article, the Kullback–Leibler divergence was added to the loss function to improve the ability of feature extraction and the ability to recover raw data. Finally, this model was compared with the traditional data dimensionality reduction method (principal component analysis (PCA), independent component analysis, and singular value decomposition) on gas pipeline dataset. The results show that the approach designed in this article outperforms the three methods in different scenes in terms of f1 score.

Publisher

Frontiers Media SA

Subject

Economics and Econometrics,Energy Engineering and Power Technology,Fuel Technology,Renewable Energy, Sustainability and the Environment

Reference26 articles.

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4. Evaluation of machine learning-based anomaly detection algorithms on an industrial modbus/tcp data set;Anton,2018

5. Anomaly detection in industrial control systems using logical analysis of data;Das;Comput. Secur,2020

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