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General Information
Editor-in-chief
Prof. Wael Badawy
Department of Computing and Information Systems Umm Al Qura University, Canada
I'm happy to take on the position of editor in chief of IJCTE. We encourage authors to submit papers concerning any branch of computer theory and engineering.
IJCTE 2017Vol.9(5): 374-379 ISSN: 1793-8201
DOI: 10.7763/IJCTE.2017.V9.1169

Deep Feature Extraction for multi-Class Intrusion Detection in Industrial Control Systems

Sasanka Potluri and Christian Diedrich
Abstract—Abstract—In recent days, network based communication is more vulnerable to outsider and insider attacks due to its wide spread applications in different domains. Intrusion detection is a key task for defense-in depth strategy of the communication networks. In order to defend properly against growing threats, Industrial Control Systems (ICS) and Supervisory Control and Data Acquisition (SCADA) need to incorporate this technology. Intrusion Detection System (IDS), a software application or a hardware which is able to monitor the network traffic and find abnormal activities in the network. Due to raise in the network bandwidth and network data a deep packet inspection is necessary to extract proper features and identify the attacks. Deep Neural Networks (DNN) a deep learning approach is used in this paper to identify the different types of attacks in the network packets. NSL-KDD dataset is used to evaluate the effectiveness of the proposed IDS. Experimental results show that the features extracted using DNN provides a better classification accuracy than the conventional machine learning techniques.

Index Terms—Index Terms—Deep learning, intrusion detection system, industrial control system, network security, deep neural networks.

Sasanka Potluri and Christian Diedrich are with Institute for Automation Engineering, Otto-von-Guericke University Magdeburg, 39106 Magdeburg, Germany (e-mail: sasanka.potluri@ovgu.de, christian.diedrich@ovgu.de).

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Cite:Sasanka Potluri and Christian Diedrich, "Deep Feature Extraction for multi-Class Intrusion Detection in Industrial Control Systems," International Journal of Computer Theory and Engineering vol. 9, no.5, pp. 374-379 , 2017.

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