New Machine Learning Findings Has Been Reported by Investigators at University of Ulsan (Unsupervised Machine Learning-based Detection of Covert Data Integrity Assault In Smart Grid Networks Utilizing Isolation Forest)
- Oct 9, 2019 10:38 am GMT
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2019 OCT 08 (NewsRx) -- By a News Reporter-Staff News Editor at Information Technology Daily -- Fresh data on Machine Learning are presented in a new report. According to news reporting from Ulsan, South Korea, by NewsRx journalists, research stated, “Being one of the most multifaceted cyber-physical systems, smart grids (SGs) are arguably more prone to cyber-threats. A covert data integrity assault (CDIA) on a communications network may be lethal to the reliability and safety of SG operations.”
Financial support for this research came from National Research Foundation of Korea (NRF) grant through the Korean Government (MSIT).
The news correspondents obtained a quote from the research from the University of Ulsan, “They are intelligently designed to sidestep the traditional bad data detector in power control centers, and this type of assault can compromise the integrity of the data, causing a false estimation of the state that further severely distresses the entire power system operation. In this paper, we propose an unsupervised machine learning-based scheme to detect CDIAs in SG communications networks utilizing non-labeled data. The proposed scheme employs a state-of-the-art algorithm, called isolation forest, and detects CDIAs based on the hypothesis that the assault has the shortest average path length in a constructed random forest. To tackle the dimensionality issue from the growth in power systems, we use a principal component analysis-based feature extraction technique. The evaluation of the proposed scheme is carried out through standard IEEE 14-bus, 39-bus, 57-bus, and 118-bus systems.”
According to the news reporters, the research concluded: “The simulation results show that the proposed scheme is proficient at handling non-labeled historical measurement datasets and results in a significant improvement in attack detection accuracy.”
For more information on this research see: Unsupervised Machine Learning-based Detection of Covert Data Integrity Assault In Smart Grid Networks Utilizing Isolation Forest. IEEE Transactions on Information Forensics and Security, 2019;14(10):2765-2777. IEEE Transactions on Information Forensics and Security can be contacted at: Ieee-Inst Electrical Electronics Engineers Inc, 445 Hoes Lane, Piscataway, NJ 08855-4141, USA. (Institute of Electrical and Electronics Engineers - http://www.ieee.org/; IEEE Transactions on Information Forensics and Security - http://ieeexplore.ieee.org/xpl/RecentIssue.jsp?punumber=10206)
Our news journalists report that additional information may be obtained by contacting S.H. Hyun, University of Ulsan, Ulsan 44610, South Korea. Additional authors for this research include S. Ahmed, I. Koo and Y. Lee.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1109/TIFS.2019.2902822. This DOI is a link to an online electronic document that is either free or for purchase, and can be your direct source for a journal article and its citation.
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