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A novel RNN-GBRBM based feature decoder for anomaly detection technology in industrial control network

A novel RNN-GBRBM based feature decoder for anomaly detectio...

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作者： Zhang, Hua Zhu, Shixiang Ma, Xiao Zhao, Jun Shou, Zeng State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications 100876 China Data Network of Power Dispatch Safety Protection of Electric Power Supervisory Control System State Gird Jiangsu Electric Power Company Shanghai Road No 215 Nanjing Jiangsu210024 China Safety Protection of Electric Power Supervisory Control System NARI Group Corporation No. 19 Chengxin Rd Jiangning Nanjing Jiangsu211000 China Dispatching Data Network and Secondary Safty Protection Electric Power Science Research Institute State Gird Jiangsu Electric Power Company Jiangning Paweier Road No 1 Nanjing Jiangsu211103 China

As advances in networking technology help to connect industrial control networks with the Internet, the threat from spammers, attackers and criminal enterprises has also grown accordingly. However, traditional network Intrusion Detection system makes significant use of pattern matching to identify malicious behaviors and have bad performance on detecting zero-day exploits in which a new attack is employed. In this paper, a novel method of anomaly detection in industrial control network is proposed based on RNN-GBRBM feature decoder. The method employ network packets and extract high-quality features from raw features which is selected manually. A modified RNN-RBM is trained using the normal traffic in order to learn feature patterns of the normal network behaviors. Then the test traffic is analyzed against the learned normal feature pattern by using osPCA to measure the extent to which the test traffic resembles the learned feature pattern. Moreover, we design a semi-supervised incremental updating algorithm in order to improve the performance of the model continuously. Experiments show that our method is more efficient in anomaly detection than other traditional approaches for industrial control network. Copyright © 2017 The Institute of Electronics, Information and Communication Engineers.

关键词： Pattern matching

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Snort improvement on Profinet RT for Industrial control system Intrusion Detection 2

Snort improvement on Profinet RT for Industrial Control Syst...

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2nd IEEE International Conference on Computer and Communications, ICCC 2016

作者： Zihao, Feng Qin, Sujuan Huo, Xuesong Pei, Pei Ye, Liang Liming, Wang State Key Laboratory of Networking and Switching Technology Beijing University of Posts and Telecommunications Beijing China Data Network of Power Dispatch and Safety Protection of Electric Power Supervisory Control System State Gird Jiangsu Electric Power Company China Safety Protection of Electric Power Supervisory Control System NARI Group Corporation Jiangsu China Dispatching Data Network and Secondary Safety Protection Electric Power Science Research Institute of State Gird Jiangsu Electric Power Company China

ISBN: (纸本)9781467390262

In recent years, the number of information security incidents of industry controlling system has increased dramatically. The intrusion detection system for industrial control systems has received extensive attention. As one of powerful and light weight network Intrusion Detection system, Snort has good expansibility and translatability, and can be used in various environments. However, Snort has clear deficiency on the intrusion detection of Profinet RT signal. Profinet DCP is an important part of Profinet RT. The intrusion detection for Profinet DCP is very important for the industry controlling systems which use Profinet to connect. To realize the detection for Profinet RT signal, this paper expanded the snort packet decoding engine, and designed a preprocessor plug-ins for Profinet RT signal. Experimental result shows that the improved Snort can support the intrusion detection for Profinet RT signal. © 2016 IEEE.

关键词： Intrusion detection

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