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On Detecting False Data Injection with Limited Network Information using Statistical Techniques

Authors: Kush Khanna, Ranjan Bose, and Anupam Joshi

Book Title: IEEE Power and Energy Society General Meeting, Chicago, 2017

Date: November 30, 2016

Abstract: Abstract—Cyber-attacks poses a serious threat to power system operation. False data injection attack (FDIA) is one such severe threat, if wisely constructed, can cause flawed estimation of power system states, thereby, leading to uneconomical and unsecured operation of power system. In recent years many methods are proposed to secure the smart grid against malicious cyber-events by protecting certain critical measurement sensors. However, making a system completely hack-proof is rather idealistic. In this paper, in addition to the research carried out in this space, we present a new Log transformation based method to detect the FDIA in real time with high probability. The detection probability of the proposed scheme is compared with existing method using IEEE 14 bus system.

Type: InProceedings

Tags: cyber security, false data injection, kullback- leibler distance, log transformation, smart grid.

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