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|Title:||Identification of state registers of FSM through full scan by data analytics||Authors:||He, Chengkang
|Keywords:||Engineering::Electrical and electronic engineering||Issue Date:||2019||Source:||He, C., Cui, A., & Chang, C.-H. (2019). Identification of state registers of FSM through full scan by data analytics. Proceedings of the 2019 Asian Hardware Oriented Security and Trust Symposium (AsianHOST). doi:10.1109/AsianHOST47458.2019.9006677||Project:||MOE2018-T1-001-131 (RG87/18)||Abstract:||Finite-state machine (FSM) is widely used as control unit in most digital designs. Many intellectual property protection and obfuscation techniques leverage on the exponential number of possible states and state transitions of large FSM to secure a physical design with the reason that it is challenging to retrieve the FSM design from its downstream design or physical implementation without knowledge of the design. In this paper, we postulate that this assumption may not be sustainable with big data analytics. We demonstrate by applying a data mining technique to analyze sufficiently large amount of data collected from a full scan design to identify its FSM state registers. An impact metric is introduced to discriminate FSM state registers from other registers. A decision tree algorithm is constructed from the scan data for the regression analysis of the dependency of other registers on a chosen register to deduce its impact. The registers with the greater impact are more likely to be the FSM state registers. The proposed scheme is applied on several complex designs from OpenCores. The experiment results show the feasibility of our scheme in correctly identifying most FSM state registers with a high hit rate for a large majority of the designs.||URI:||https://hdl.handle.net/10356/145849||ISBN:||978-1-7281-3544-1||DOI:||10.1109/AsianHOST47458.2019.9006677||Rights:||© 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at: https://doi.org/10.1109/AsianHOST47458.2019.9006677||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||EEE Conference Papers|
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