Attacking Split Manufacturing from a Deep Learning Perspective
Refereed conference paper presented and published in conference proceedings

替代計量分析
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其它資訊
摘要The notion of integrated circuit split manufacturing which delegates the front-end-of-line (FEOL) and back-end-of-line (BEOL) parts to different foundries, is to prevent overproduction, piracy of the intellectual property (IP), or targeted insertion of hardware Trojans by adversaries in the FEOL facility. In this work, we challenge the security promise of split manufacturing by formulating various layout-level placement and routing hints as vector-and image-based features. We construct a sophisticated deep neural network which can infer the missing BEOL connections with high accuracy. Compared with the publicly available network-flow attack [1], for the same set of ISCAS-85 benchmarks, we achieve 1.21x accuracy when splitting on M1 and 1.12x accuracy when splitting on M3 with less than 1% running time.
著者Haocheng Li, Satwik Patnaik, Abhrajit Sengupta, Haoyu Yang, Johann Knechtel, Bei Yu, Evangeline F. Y. Young, Ozgur Sinanoglu
會議名稱56th ACM/EDAC/IEEE Design Automation Conference (DAC)
會議開始日02.06.2019
會議完結日06.06.2019
會議地點Las Vegas, NV
會議國家/地區美國
會議論文集題名DAC '19 Proceedings of the 56th Annual Design Automation Conference 2019
出版年份2019
月份6
國際標準書號978-1-4503-6725-7
語言美式英語

上次更新時間 2020-19-01 於 02:26