During the last decade, the Integrated Circuit industry has paid special attention to the security of products. Hardware-based vulnerabilities, in particular Hardware Trojans, are becoming a serious threat, pushing the research community to provide highly sophisticated techniques to detect them. Despite the considerable effort that has been invested in this area, the growing complexity of modern devices always calls for sharper detection methodologies. This paper illustrates a pre-silicon simulation-based technique to detect hardware trojans. The technique exploits well-established machine learning algorithms. The paper introduces all the background concepts and presents the methodology. The validity of the approach has been demonstrated on the AutoSoC CPU, an industrial-grade, safety-oriented, automotive benchmark suite. Experimental results demonstrate the applicability and effectiveness of the approach: the proposed technique is highly accurate in pinpointing suspicious code sections. None of the hardware trojans from the set has been left undetected.

Machine Learning for Hardware Security: Classifier-based Identification of Trojans in Pipelined Microprocessors / Damljanovic, Aleksa; Ruospo, Annachiara; Ernesto, Sanchez; Squillero, Giovanni. - In: APPLIED SOFT COMPUTING. - ISSN 1568-4946. - ELETTRONICO. - 116:(2022), pp. 1-16. [https://doi.org/10.1016/j.asoc.2021.108068]

Machine Learning for Hardware Security: Classifier-based Identification of Trojans in Pipelined Microprocessors

Aleksa Damljanovic;Annachiara Ruospo;Ernesto Sanchez;Giovanni Squillero
2022

Abstract

During the last decade, the Integrated Circuit industry has paid special attention to the security of products. Hardware-based vulnerabilities, in particular Hardware Trojans, are becoming a serious threat, pushing the research community to provide highly sophisticated techniques to detect them. Despite the considerable effort that has been invested in this area, the growing complexity of modern devices always calls for sharper detection methodologies. This paper illustrates a pre-silicon simulation-based technique to detect hardware trojans. The technique exploits well-established machine learning algorithms. The paper introduces all the background concepts and presents the methodology. The validity of the approach has been demonstrated on the AutoSoC CPU, an industrial-grade, safety-oriented, automotive benchmark suite. Experimental results demonstrate the applicability and effectiveness of the approach: the proposed technique is highly accurate in pinpointing suspicious code sections. None of the hardware trojans from the set has been left undetected.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2941732