@conference{Efstathopoulos2019,

title = {Operational data based intrusion detection system for smart grid},

author = { G. Efstathopoulos and P.R. Grammatikis and P. Sarigiannidis and V. Argyriou and A. Sarigiannidis and K. Stamatakis and M.K. Angelopoulos and S.K. Athanasopoulos},

url = {https://www.researchgate.net/publication/335866997_Operational_Data_Based_Intrusion_Detection_System_for_Smart_Grid},

doi = {10.1109/CAMAD.2019.8858503},

year  = {2019},

date = {2019-01-01},

journal = {IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks, CAMAD},

volume = {2019-September},

abstract = {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. © 2019 IEEE.},

keywords = {Anomaly Detection, Cybersecurity, intrusion detection system, machine learning, Operational Data, Smart Grid},

pubstate = {published},

tppubtype = {conference}

}