Visible to the public High Efficiency Early-Complete Brute Force Elimination Method for Security Analysis of Camouflage IC

TitleHigh Efficiency Early-Complete Brute Force Elimination Method for Security Analysis of Camouflage IC
Publication TypeConference Paper
Year of Publication2020
AuthorsHo, W.-G., Ng, C.-S., Kyaw, N. A., Lwin, N. Kyaw Zwa, Chong, K.-S., Gwee, B.-H.
Conference Name2020 IEEE Asia Pacific Conference on Circuits and Systems (APCCAS)
Date PublishedDec. 2020
PublisherIEEE
ISBN Number978-1-7281-9396-0
KeywordsBenchmark testing, Brute Force Attack, brute force attacks, Camouflage Integrated Circuit (IC), Force, High Efficiency, Human Behavior, human factors, integrated circuits, Intellectual Property (IP) Protection, Logic gates, policy-based governance, pubcrawl, reverse engineering, security, simulation
Abstract

We propose a high efficiency Early-Complete Brute Force Elimination method that speeds up the analysis flow of the Camouflage Integrated Circuit (IC). The proposed method is targeted for security qualification of the Camouflaged IC netlists in Intellectual Property (IP) protection. There are two main features in the proposed method. First, the proposed method features immediate elimination of the incorrect Camouflage gates combination for the rest of computation, concentrating the resources into other potential correct Camouflage gates combination. Second, the proposed method features early complete, i.e. revealing the correct Camouflage gates once all incorrect gates combination are eliminated, increasing the computation speed for the overall security analysis. Based on the Python programming platform, we implement the algorithm of the proposed method and test it for three circuits including ISCAS'89 benchmarks. From the simulation results, our proposed method, on average, features 71% lesser number of trials and 79% shorter run time as compared to the conventional method in revealing the correct Camouflage gates from the Camouflaged IC netlist.

URLhttps://ieeexplore.ieee.org/document/9301666
DOI10.1109/APCCAS50809.2020.9301666
Citation Keyho_high_2020