A Unified Hardware-based Threat Detector for AI Accelerators

• URL: http://arxiv.org/abs/2311.16684v1
• Date: Tue, 28 Nov 2023 10:55:02 GMT
• Title: A Unified Hardware-based Threat Detector for AI Accelerators
• Authors: Xiaobei Yan, Han Qiu, Tianwei Zhang,
• Abstract summary: We design UniGuard, a novel unified and non-intrusive detection methodology to safeguard FPGA-based AI accelerators. We employ a Time-to-Digital Converter to capture power fluctuations and train a supervised machine learning model to identify various types of threats.
• Score: 12.96840649714218
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The proliferation of AI technology gives rise to a variety of security threats, which significantly compromise the confidentiality and integrity of AI models and applications. Existing software-based solutions mainly target one specific attack, and require the implementation into the models, rendering them less practical. We design UniGuard, a novel unified and non-intrusive detection methodology to safeguard FPGA-based AI accelerators. The core idea of UniGuard is to harness power side-channel information generated during model inference to spot any anomaly. We employ a Time-to-Digital Converter to capture power fluctuations and train a supervised machine learning model to identify various types of threats. Evaluations demonstrate that UniGuard can achieve 94.0% attack detection accuracy, with high generalization over unknown or adaptive attacks and robustness against varied configurations (e.g., sensor frequency and location).

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