Publication:
Is your FPGA bitstream Hardware Trojan-free? Machine learning can provide an answer

Loading...
Thumbnail Image

Advisors

Tutors

Editor

Publication date

Defense date

Journal Title

Journal ISSN

Volume Title

Publisher

Elsevier

publication.page.ispartofseries

Creative Commons license

Impact
Google Scholar
Export

Research Projects

Research Projects

Organizational Units

Journal Issue

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.

Note

Bibliographic citation

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.

Table of contents

Has version

Is version of

Related dataset

Related Publication

Is part of

Collections