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

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https://ieeexplore.ieee.org/document/8273902

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10.1109/PESGM.2017.8273902
 http://hdl.handle.net/11603/11598

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UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection

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2018-02-01

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conference paper pre-print
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Citation of Original Publication

Kush Khanna, Ranjan Bose, Anupam Joshi, On Detecting False Data Injection with Limited Network Information using Statistical Techniques, 2017 IEEE Power & Energy Society General Meeting ,01 Feb 2018, , DOI: 10.1109/PESGM.2017.8273902 .

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Subjects

Cyber security
false data injection
Kullback- Leibler distance
log transformation
smart grid
Time measurement
IEEE 14 bus system
network information
critical measurement sensors
UMBC Ebiquity Research Group

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.