Abstract
With the advent of 21st Century, we stepped into the fourth industrial revolution of cyber physical systems. There is the need of secured network systems and intrusion detection systems in order to detect network attacks. Use of machine learning for anomaly detection in industrial networks faces challenges which restricts its large-scale commercial deployment. ADIN Suite proposes a roadmap to overcome these challenges with multi-module solution. It solves the need for real world network traffic, an adaptive hybrid analysis to reduce error rates in diverse network traffic and alarm correlation for semantic description of detection results to the network operator.
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References
Platform Industrie 4.0. Cooperation between plattform industrie 4.0 and industrial internet consortium. 2016.
Platform Industrie 4.0. Network-based communication for industrie 4.0. Discussion paper, Federal Ministry for Economic Affairs and Energy (BMWi), Germany, 2016.
Dzung, Dacfey and Naedele, Martin and Von Hoff, Thomas P and Crevatin, Mario. Security for industrial communication systems. Proceedings of the IEEE, 93(6):1152-1177, 2005.
Knapp, Eric D and Langill, Joel Thomas. Industrial Network Security: Securing critical infrastructure networks for smart grid, SCADA, and other Industrial Control Systems. Syngress, 2014.
Brendan Galloway and Gerhard P Hancke. Introduction to industrial control networks. IEEE Communications surveys & tutorials, 15(2):860–880.
Scarfone, Karen and Mell, Peter. Guide to intrusion detection and prevention systems (idps). NIST special publication, 800(2007):94, 2007.
Buczak, Anna L and Guven, Erhan. A survey of data mining and machine learning methods for cyber security intrusion detection. IEEE Communications Surveys & Tutorials, 18(2):1153-1176, 2015.
Chandola, Varun and Banerjee, Arindam and Kumar, Vipin. Anomaly detection: A survey. ACM computing surveys (CSUR), 41(3):15, 2009.
Patcha, Animesh and Park, Jung-Min. An overview of anomaly detection techniques: Existing solutions and latest technological trends. Computer networks, 51(12):3448–3470, 2007.
Sommer, Robin and Paxson, Vern. Outside the closed world: On using machine learning for network intrusion detection. In 2010 IEEE symposium on security and privacy, pages 305-316. IEEE, 2010.
Dorothy E. Denning. An intrusion-detection model. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 13(2):222–232, 1987.
Lippmann, Richard and Haines, Joshua W and Fried, David J and Korba, Jonathan and Das, Kumar. The 1999 darpa on-line intrusion detection evaluation. Computer networks, 34(4):579-595, 2000.
KDD Cup. Dataset. available at the following website http://kdd.ics.uci.edu/databases/kddcup99/kddcup99.html, 1999.
Tavallaee, Mahbod and Bagheri, Ebrahim and Lu, Wei and Ghorbani, Ali-A. A detailed analysis of the kdd cup 99 data set. In Proceedings of the Second IEEE Symposium on Computational Intelligence for Security and Defence Applications 2009, 2009.
Pfrang, S. and Kippe, J. and Meier, D. and Haas, C. Design and Architecture of an Industrial IT Security Lab. In TRIDENTCOM 2016 - Proceedings of the 11th International Conference on Testbeds and research infrastructures for the development of networks & communities (to appear), 2016.
Ahonen, Pasi. Constructing network security monitoring systems. Vtt Research Notes, 2011.
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Meshram, A., Haas, C. (2017). Anomaly Detection in Industrial Networks using Machine Learning: A Roadmap. In: Beyerer, J., Niggemann, O., Kühnert, C. (eds) Machine Learning for Cyber Physical Systems. Technologien für die intelligente Automation. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-53806-7_8
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DOI: https://doi.org/10.1007/978-3-662-53806-7_8
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