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S3: Sneaky Spectral Strike Trojan Attacks on Deep Learning-based Time Series Smart Grid Models
  • Sultan Uddin Khan ,
  • Mohammed Mynuddin,
  • Mahmoud Nabil
Sultan Uddin Khan

Corresponding Author:[email protected]

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Mohammed Mynuddin
Mahmoud Nabil

Abstract

Deep learning (DL) has gained prominence as an effective approach for enhancing the efficiency of various applications including smart grids (SG). Although these models excel significantly in the classification tasks of power quality disturbances, their vulnerability to trojan attacks introduces potential complications. In this paper, we introduce two novel algorithms for executing trojan attacks on DL models handling time series data in SG, tailored for both white-box and black-box. For white-box, our algorithm titled 'Sneaky Spectral Strike (S 3)' utilizes the frequency domain and trigger optimization to perform trojan attacks, which demonstrates a remarkable average fooling rate of 99.9% across various DL models. The algorithm also balances the signal-to-noise ratio, trojan model accuracy on clean data, and fooling rate to be highly effective in fooling DL model and imperceptible to human observers in the power control center (PCC). For black-box, we propose a novel algorithm, 'Lite Datanet Sneaky Spectral Strike', that integrates a simple DL model with a small sample dataset to create trojan triggers that are highly effective, stealthy, and transferable to the DL model deployed in PCC. This approach achieves a 99.86% average fooling rate for different advanced DL models, highlighting the effectiveness of resource-efficient strategies in DL-based SG. Both algorithms underscore the potential vulnerabilities in DL models used in SG , and mark a significant advancement in adversarial machine learning.
27 Dec 2023Submitted to TechRxiv
02 Jan 2024Published in TechRxiv