Large Language Model for Smart Inverter Cyber-Attack Detection via Textual Analysis of Volt/VAR Commands

This letter demonstrates a proof-of-concept validation of the Large Language Model (LLM) for smart inverter cyberattack detection through textual control commands. The proposed method can detect the manipulation of Volt/VAR curves and the comparison results with state-of-the-art machine learning techniques highlight its efficacy in identifying cyber attacks. Test results obtained from a real distribution feeder in Colorado, USA, validate the high accuracy for attack classification as well as demonstrate the potential of LLMs in adding a robust security layer for cyber-attacks.

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Publication Details

Journal
IEEE Transactions on Smart Grid
Published
2024-09-02
DOI
https://doi.org/10.1109/tsg.2024.3453648
Citations
18
Primary Topic
Advanced Malware Detection Techniques
Type
article
Field-Weighted Citation Impact
6.30

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article

Large Language Model for Smart Inverter Cyber-Attack Detection via Textual Analysis of Volt/VAR Commands

Junbo Zhao, Alaa Selim, Bo Yang
18 citations
IEEE Transactions on Smart Grid
Advanced Malware Detection Techniques
6.30
article

Large Language Model for Smart Inverter Cyber-Attack Detection via Textual Analysis of Volt/VAR Commands

Junbo Zhao, Alaa Selim, Bo Yang
article en
18 citations

Abstract

This letter demonstrates a proof-of-concept validation of the Large Language Model (LLM) for smart inverter cyberattack detection through textual control commands. The proposed method can detect the manipulation of Volt/VAR curves and the comparison results with state-of-the-art machine learning techniques highlight its efficacy in identifying cyber attacks. Test results obtained from a real distribution feeder in Colorado, USA, validate the high accuracy for attack classification as well as demonstrate the potential of LLMs in adding a robust security layer for cyber-attacks.

IEEE Transactions on Smart GridVol. 15(6)
University of Connecticut (US), Hitachi Global Storage Technologies (United States) (US)
Office of Cybersecurity, Energy Security, and Emergency Response
Openalex Percentile: Top 3%
Advanced Malware Detection Techniques
6.30
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