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Intelligent analysis of digital evidences in large-scale logs in power systems attributed to the attacks
KTH, School of Electrical Engineering and Computer Science (EECS), Network and Systems Engineering.
Department of Information Security & Communication Technology, Norwegian University of Science and Technology, Norway.
KTH, School of Electrical Engineering and Computer Science (EECS), Electric Power and Energy Systems.
2018 (English)In: Proceedings - 2018 IEEE International Conference on Big Data, Big Data 2018, 2018, p. 3087-3092Conference paper, Published paper (Refereed)
Abstract [en]

Smart grid improves and revolutionizes the way how energy is generated, distributed and consumed. Despite utilization of such technologies for better life of end-users and communities, there might be outlier events happening that will introduce disturbance to the smart grids. To mitigate impact from such events in power grid, particularly in Wide Area Monitoring Protection and Control (WAMPAC) has been introduced for mitigation and prevention of large disruption and extreme events. Large network of interconnected devices is being monitored through WAMPAC sub-system to avoid major events with negative impact through analysis of system-wide contextual information. The assessment of the state is being made based on the data from Phasor Measurement Unit (PMUs) collected and processed in the Phasor Data Concentrator (PDC). There is an enormous amount of Machine-to-Machine (M2M) communication that the system has to analyze. However, blackout prediction and mitigation is done using measurements data and does not necessarily focus on more high level adversarial events. This paper proposes an ongoing research into timely detection of adversarial attack on the power grid.  During the experimental phase, authentication attack scenario was successfully executed on power substation setup. Further, framework for intelligent identification of digital evidences related to attack was suggested unveiling possibility for crime investigations preparedness.

Place, publisher, year, edition, pages
2018. p. 3087-3092
Keywords [en]
Big Data Applications; Forensic Investigations; Forensic Artifacts; Evidence; PMUs; Machine Learning; Wide Area Monitoring Protection and Control
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Research subject
Electrical Engineering
Identifiers
URN: urn:nbn:se:kth:diva-240577DOI: 10.1109/BigData.2018.8622220ISI: 000468499303021Scopus ID: 2-s2.0-85062643351ISBN: 978-1-5386-5035-6 (electronic)OAI: oai:DiVA.org:kth-240577DiVA, id: diva2:1272781
Conference
2018 IEEE International Conference on Big Data, Big Data 2018; Seattle; United States; 10 December 2018 through 13 December 2018
Funder
Swedish Civil Contingencies Agency
Note

QC 20190108

Available from: 2018-12-19 Created: 2018-12-19 Last updated: 2019-06-26Bibliographically approved

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CiteExportLink to record
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Output format
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