New Technology Study Results Reported from Chongqing University of Posts and Telecommunications (Detection of False Data Injection Attacks In Smart Grid Utilizing Elm-based Ocon Framework)
- May 13, 2019
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2019 MAY 10 (NewsRx) -- By a News Reporter-Staff News Editor at Tech Daily News -- Data detailed on Technology have been presented. According to news originating from Chongqing, People’s Republic of China, by NewsRx editors, the research stated, “False data injection (I-DI) attacks, as a new class of cyberattacks, bring a severe threat to the security and reliable operation of the smart grid by damaging the state estimation of the power system. To address this issue, an extreme learning machine (ELM)-based one-class-one-network (OCON) framework is proposed for detecting the FDI attacks in this paper.”
Funders for this research include National Natural Science Foundation of China, Innovation Project of the Common Key Technology of Chongqing Major Industry, Program for Changjiang Scholars and Innovative Research Team in University.
Our news journalists obtained a quote from the research from the Chongqing University of Posts and Telecommunications, “Under this framework, to effectively detect bus-based FDI attacks and identify the bus node being attacked, the subnets of state identification layer in OCON adopt the ELM algorithm to accurately divide the false data and the normal data. After that, a global layer is employed to analyze whether the bus node associated with its corresponding subnet is attacked by false data utilizing the results from the state identification layer. Finally, in order to improve the resilience of the power system, a prediction recovery strategy is proposed to remedy the detected false data by exploiting the spatial correlation of power data. The proposed framework is tested on the IEEE 14 bus system using real load data from New York independent system operator.”
According to the news editors, the research concluded: “The simulation results demonstrate that the proposed framework not only accurately recognizes the multiple bus nodes under the FDI attacks but also efficiently recovers the data injected by false data.”
For more information on this research see: Detection of False Data Injection Attacks In Smart Grid Utilizing Elm-based Ocon Framework. IEEE Access, 2019;7():31762-31773. IEEE Access can be contacted at: Ieee-Inst Electrical Electronics Engineers Inc, 445 Hoes Lane, Piscataway, NJ 08855-4141, USA.
The news correspondents report that additional information may be obtained from X.R. Jing, Chongqing University of Posts and Telecommunications, School of Communications and Information Engineering, Chongqing 400065, People’s Republic of China.
The direct object identifier (DOI) for that additional information is: https://doi.org/10.1109/ACCESS.2019.2902910. 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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