Autoencoder Based Anomaly Detection for SCADA Networks

Journal article

Nazir, S., Patel, S. and Patel, D. (2021). Autoencoder Based Anomaly Detection for SCADA Networks. International Journal of Artificial Intelligence and Machine Learning. 11 (2), pp. 83-99. https://doi.org/10.4018/ijaiml.20210701.oa6
AuthorsNazir, S., Patel, S. and Patel, D.
AbstractSupervisory control and data acquisition (SCADA) systems are industrial control systems that are used to monitor critical infrastructures such as airports, transport, health, and public services of national importance. These are cyber physical systems, which are increasingly integrated with networks and internet of things devices. However, this results in a larger attack surface for cyber threats, making it important to identify and thwart cyber-attacks by detecting anomalous network traffic patterns. Compared to other techniques, as well as detecting known attack patterns, machine learning can also detect new and evolving threats. Autoencoders are a type of neural network that generates a compressed representation of its input data and through reconstruction loss of inputs can help identify anomalous data. This paper proposes the use of autoencoders for unsupervised anomaly-based intrusion detection using an appropriate differentiating threshold from the loss distribution and demonstrate improvements in results compared to other techniques for SCADA gas pipeline dataset.
Year2021
JournalInternational Journal of Artificial Intelligence and Machine Learning
Journal citation11 (2), pp. 83-99
PublisherIGI Global
ISSN2642-1577
2642-1585
Digital Object Identifier (DOI)https://doi.org/10.4018/ijaiml.20210701.oa6
Publication dates
Print2021
Publication process dates
Deposited14 Oct 2021
Publisher's version
License
File Access Level
Open
Permalink -

https://openresearch.lsbu.ac.uk/item/8wqwv

Download files

Publisher's version
Autoencoder-Based-Anomaly-Detection-for-SCADA-Networks.pdf
License: CC BY 4.0
File access level: Open

  • 57
    total views
  • 205
    total downloads
  • 3
    views this month
  • 0
    downloads this month

Export as

Related outputs

The Cognitive and Mathematical Foundations of Analytic Epidemiology
Patel, S., Wang, Y., Platanioti, K.N., Wang, J.Z., Hou, M., Zhou, M., Howard, N., Peng, J., Huang, R. and Zhang, D. (2020). The Cognitive and Mathematical Foundations of Analytic Epidemiology. 19th IEEE International Conference on Cognitive Informatics and Cognitive Computing. Beijing, China 26 - 28 Sep 2020 Institute of Electrical and Electronics Engineers (IEEE).
Assessing Hyper Parameter Optimization and Speedup for Convolutional Neural Networks
Nazir, S., Patel, S. and Patel, D. (2020). Assessing Hyper Parameter Optimization and Speedup for Convolutional Neural Networks. International Journal of Artificial Intelligence and Machine Learning. 10 (2), pp. 1-17. https://doi.org/10.4018/ijaiml.2020070101
Cloud-based Autonomic Computing Framework for Securing SCADA Systems
Patel, S., Patel, D. and Nazir, S. (2020). Cloud-based Autonomic Computing Framework for Securing SCADA Systems. in: Chui, K.T., Lytras, M.D., Liu, R.W. and Zhao, M. (ed.) Innovations, Algorithms, and Applications in Cognitive Informatics and Natural Intelligence IGI Global.
A Critical Analysis Of ‘Creativity’ In Sustainable Production And Design
Empson, T., Chance, S. and Patel, S. (2019). A Critical Analysis Of ‘Creativity’ In Sustainable Production And Design. 21st International Conference on Engineering and Product Design Education . University of Strathclyde, Glasgow 12 - 13 Sep 2019 https://doi.org/10.35199/epde2019.4
On Autonomous Systems: From Reflexive, Imperative and Adaptive Intelligence to Autonomous and Cognitive Intelligence
Patel, S., Wang, Y., Plataniotis, K.N., Kwong, S., Leung, H., Yanushkevich, S., Karray, F., Hou, M., Howard, N., Fiorini, R.A., Soda, P. and Tunstel, E. (2019). On Autonomous Systems: From Reflexive, Imperative and Adaptive Intelligence to Autonomous and Cognitive Intelligence. IEEE International Conference on Cognitive Informatics & Cognitive Computing 2019. Milan, Italy 23 - 25 Jul 2019 Institute of Electrical and Electronics Engineers (IEEE).
Cognitive Informatics
Wang, Y., Howard, N., Kacprzyk, J., Frieder, O., Sheu, P., Fiorini, R.A., Gavrilova, M.L., Patel, S., Peng, J. and Widrow, B. (2018). Cognitive Informatics. International Journal of Cognitive Informatics and Natural Intelligence. 12 (1), pp. 1-13. https://doi.org/10.4018/ijcini.2018010101
Formal Ontology Generation by Deep Machine Learning
Wang, Y, Valipour, M, Zatarain, O, Gavrilova, M, Hussain, A, Howard, N and Patel, S. (2018). Formal Ontology Generation by Deep Machine Learning. Cognitive Informatics & Cognitive Computing (ICCI*CC), 2017 IEEE 16th International Conference on. Oxford 26 - 28 Jul 2017 Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICCI-CC.2017.8109723
Hyper Parameters Selection for Image Classification in Convolutional Neural Networks
Patel, S. (2018). Hyper Parameters Selection for Image Classification in Convolutional Neural Networks. IEEE International Conference on Cognitive Informatics & Cognitive Computing 2018. Berkeley, Califormia, USA 15 - 18 Jul 2018 Institute of Electrical and Electronics Engineers (IEEE).
A Survey and Analysis on Sequence Learning Methodologies and Deep Neural Networks
Patel, S., Wang, Y, Zatarain, O, Graves, D, Gavrilova, M and Howard, N (2018). A Survey and Analysis on Sequence Learning Methodologies and Deep Neural Networks. IEEE International Conferenece on Cognitive Informatics & Cognitive Computing. Berkeley, California, USA 16 - 18 Jul 2018 Institute of Electrical and Electronics Engineers (IEEE).
Towards a Business Model Framework to Increase Collaboration in the Freight Industry
Vargas, A, Patel, S. and Patel, D. (2018). Towards a Business Model Framework to Increase Collaboration in the Freight Industry. Logistics. 2 (4), p. 22. https://doi.org/10.3390/logistics2040022
Abstract intelligence: Embodying and enabling cognitive systems by mathematical engineering
Patel, S. (2017). Abstract intelligence: Embodying and enabling cognitive systems by mathematical engineering. International Journal of Cognitive Informatics and Natural Intelligence. 11 (1), pp. 1-15. https://doi.org/10.4018/IJCINI.2017010101
Electrochemical Membrane Technology for Carbon Dioxide Capture from Flue Gas
Ghezel-Ayagh, H, Jolly, S, Patel, D. and Steen, W (2017). Electrochemical Membrane Technology for Carbon Dioxide Capture from Flue Gas. Energy Procedia. 108, pp. 2-9. https://doi.org/10.1016/j.egypro.2016.12.183
Assessing and Augmenting SCADA Cyber Security-A Survey of Techniques
Patel, S., Nazir, S and Patel, D. (2017). Assessing and Augmenting SCADA Cyber Security-A Survey of Techniques. Computers & Security. 70, pp. 436-454. https://doi.org/10.1016/j.cose.2017.06.010
Autonomic Computing Architecture for SCADA Cyber Security
Patel, S., Nazir, S and Patel, D. (2017). Autonomic Computing Architecture for SCADA Cyber Security. International Journal of Cognitive Informatics and Natural Intelligence. 11 (4). https://doi.org/10.4018/IJCINI.2017100104
Stimulating intellectual activity with adaptive environment (SMILE)
Gusev, M, Patel, S. and Tasic, J (2017). Stimulating intellectual activity with adaptive environment (SMILE). The 8th Balkan Conference in Informatics. Skopje, Macedonia 20 - 23 Sep 2017 London South Bank University. https://doi.org/10.1145/3136273.3136283
Autonomic computing meets SCADA security
Nazir, S, Patel, S. and Patel, D. (2017). Autonomic computing meets SCADA security. 16th International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2017. Oxford London South Bank University. pp. 498-502 https://doi.org/10.1109/ICCI-CC.2017.8109795
Quantum information processes in protein microtubules of brain neurons
Enaki, NA, Koroli, V, Bazgan, S, Nistreanu, A, Palistrant, S, Bogoev, D, Turcan, M, Pislari, T, Boshneaga, Y, Lambropoulos, N, Patel, S., Khrennikov, A, Marinucci, M, Kwok, SC, Pannese, L, Arniani, M, Torrenti, R, Maslobrod, S, Scherbakov, V, Kuznetsov, E, Moldovanu, I, Misic, O, Odobescu, S, Lupusor, A, Cernei, A, Vovc, V, Arnaut, O, Ciobanu, N, Tuzlucov, P, Kernbach, S, Sorli, A and Anisimov, V (2016). Quantum information processes in protein microtubules of brain neurons. IFMBE Proceedings. 55, pp. 245-249. https://doi.org/10.1007/978-981-287-736-9_60
Inter-enterprise architecture as a tool to empower decision-making in hierarchical collaborative production planning
Vargas, A, Boza, A, Patel, S., Patel, D., Cuenca, L and Ortiz, A (2016). Inter-enterprise architecture as a tool to empower decision-making in hierarchical collaborative production planning. Data and Knowledge Engineering. 105, pp. 5-22. https://doi.org/10.1016/j.datak.2015.10.002
Risk Management in hierarchical production planning using inter-enterprise architecture
Vargas, A, Boza, A, Patel, S., Patel, D., Cuenca, L and Ortiz, A (2015). Risk Management in hierarchical production planning using inter-enterprise architecture. 16th IFIP Working Conference on Virtual Enterprise (PRO-VE 15). Albi, France 05 - 07 Oct 2015 London South Bank University.