Publication: Is your FPGA bitstream Hardware Trojan-free? Machine learning can provide an answer
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Elsevier
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To cite this item, use the following identifier: https://hdl.handle.net/10016/35906
Abstract
Software exploitable Hardware Trojan Horses (HTHs) inserted into commercial CPUs allow the attacker to run his/her own software or to gain unauthorized privileges. Recently a novel menace raised: HTHs inserted by CAD tools. A consequence of such scenario is that HTHs must be considered a serious threat not only by academy but also by industry. In this paper we try to answer to the following question: can Machine Learning (ML) help designers of microprocessor softcores implemented onto SRAM-based FPGAs at detecting HTHs introduced by the employed CAD tool during the generation of the bitstream? We present a comparative analysis of the ability of several ML models at detecting the presence of HTHs in the bitstream by exploiting a previously performed characterization of the microprocessor softcore and an associated ML training. An experimental analysis has been carried out targeting the IBEX RISC-V microprocessor running a set of benchmark programs. A detailed comparison of multiple ML models is conducted, showing that many of them achieve accuracy above 98%, and kappa values above 0.97. By identifying the most effective ML models and the best features to be employed, this paper lays the foundation for the integration of a ML-based bitstream verification flow.
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Palumbo, A., Cassano, L., Luzzi, B., Hernández, J. A., Reviriego, P., Bianchi, G. & Ottavi, M. (2022, julio). Is your FPGA bitstream Hardware Trojan-free? Machine learning can provide an answer. Journal of Systems Architecture, 128, 102543.