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Dreal: Detecting Side-Channel Attacks at Real-Time using Low-Level Hardware Features

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Conference Paper

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University of California Davis United States

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Prior studies on detection of SCAs based on low-level microarchitectural features captured from processors hardware performance counter HPC registers have considered collecting hardware events of both victim applications cryptographic application, e.g. RSA, AES and etc. and attack applications. However, as shown in recent works the attack HPCs data can be easily corrupted which results in misleading the SCA detection method. Furthermore, the prior works have explored the suitability of a limited number of Machine Learning ML algorithms in detecting SCAs without examining the instance level false alarm rate that as we show in this work is a more important evaluation metric for SCA detection techniques. In response, in this paper, we propose DREAL, a customized machine learning-based real-time side-channel attack detection methodology using low level hardware features captured from HPC registers. The experimental results indicate that with the proposed detection methodology, we can achieve up to 97 percent interval prediction accuracy eliminating the need for profiling SCAs. Furthermore, DREAL detection methodology can obtain 100 percent attack detection accuracy with 0 instance level false alarm rate.

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  • Computer Systems Management and Standards

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