Visible to the public Operational Data Based Intrusion Detection System for Smart Grid

TitleOperational Data Based Intrusion Detection System for Smart Grid
Publication TypeConference Paper
Year of Publication2019
AuthorsEfstathopoulos, G., Grammatikis, P. R., Sarigiannidis, P., Argyriou, V., Sarigiannidis, A., Stamatakis, K., Angelopoulos, M. K., Athanasopoulos, S. K.
Conference Name2019 IEEE 24th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)
Date PublishedSept. 2019
ISBN Number978-1-7281-1016-5
Keywordsanomaly detection, anomaly-based IDS, Collaboration, complex data representation, composability, Computer crime, cyberattacks, cybersecurity, cyberthreats, Deep Learning, distributed generation, distributed power generation, electrical grid, Electricity, energy consumers, Human Behavior, industrial control, industrial control systems, information and communication technology, Internet of Things, intrusion detection system, IoT, learning (artificial intelligence), machine learning, Metrics, Monitoring, Operational Data, operational data based intrusion detection system, pervasive control, policy-based governance, power engineering computing, power generation, power system security, privacy, Protocols, pubcrawl, remote monitoring, resilience, Resiliency, Scalability, security challenges, security of data, Smart grid, smart grid consumer privacy, smart meters, smart power grids, smart technologies, utility companies

With the rapid progression of Information and Communication Technology (ICT) and especially of Internet of Things (IoT), the conventional electrical grid is transformed into a new intelligent paradigm, known as Smart Grid (SG). SG provides significant benefits both for utility companies and energy consumers such as the two-way communication (both electricity and information), distributed generation, remote monitoring, self-healing and pervasive control. However, at the same time, this dependence introduces new security challenges, since SG inherits the vulnerabilities of multiple heterogeneous, co-existing legacy and smart technologies, such as IoT and Industrial Control Systems (ICS). An effective countermeasure against the various cyberthreats in SG is the Intrusion Detection System (IDS), informing the operator timely about the possible cyberattacks and anomalies. In this paper, we provide an anomaly-based IDS especially designed for SG utilising operational data from a real power plant. In particular, many machine learning and deep learning models were deployed, introducing novel parameters and feature representations in a comparative study. The evaluation analysis demonstrated the efficacy of the proposed IDS and the improvement due to the suggested complex data representation.

Citation Keyefstathopoulos_operational_2019