Difference between revisions of "Wei2012"
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|abstract=<p>Modern hardware security has a very broad scope ranging from digital rights management to the detection of ghost circuitry. These and many other security tasks are greatly hindered by process variation, which makes each integrated circuit (IC) unique, and device aging, which evolves the IC throughout its lifetime. We have developed a singular value decomposition (SVD)- based procedure for gate-level characterization (GLC) that calculates changes in properties, such as delay and switching power of each gate of an IC, accounting for process variation and device aging. We employ our SVD-based GLC approach for the development of two security applications: hardware metering and ghost circuitry (GC) detection. We present the first robust and low-cost hardware metering scheme, using an overlapping IC partitioning approach that enables rapid and scalable treatment. We also map the GC detection problem into an equivalent task of GLC consistency checking using the same overlapping partitioning. The effectiveness of the approaches is evaluated using the ISCAS85, ISCAS89, and ITC99 benchmarks. In hardware metering, we are able to obtain probabilities of coincidence in the magnitude of 10 or less, and we obtain zero false positives and zero false negatives in GC detection.</p> | |abstract=<p>Modern hardware security has a very broad scope ranging from digital rights management to the detection of ghost circuitry. These and many other security tasks are greatly hindered by process variation, which makes each integrated circuit (IC) unique, and device aging, which evolves the IC throughout its lifetime. We have developed a singular value decomposition (SVD)- based procedure for gate-level characterization (GLC) that calculates changes in properties, such as delay and switching power of each gate of an IC, accounting for process variation and device aging. We employ our SVD-based GLC approach for the development of two security applications: hardware metering and ghost circuitry (GC) detection. We present the first robust and low-cost hardware metering scheme, using an overlapping IC partitioning approach that enables rapid and scalable treatment. We also map the GC detection problem into an equivalent task of GLC consistency checking using the same overlapping partitioning. The effectiveness of the approaches is evaluated using the ISCAS85, ISCAS89, and ITC99 benchmarks. In hardware metering, we are able to obtain probabilities of coincidence in the magnitude of 10 or less, and we obtain zero false positives and zero false negatives in GC detection.</p> | ||
|pages=765-773 | |pages=765-773 | ||
|month=4 | |||
|year=2012 | |||
|volume=7 | |volume=7 | ||
|journal=IEEE Transactions on Information Forensics and Security | |journal=IEEE Transactions on Information Forensics and Security | ||
|title=Gate Characterization Using Singular Value Decomposition: Foundations and Applications | |title=Gate Characterization Using Singular Value Decomposition: Foundations and Applications | ||
|entry=article | |entry=article | ||
}} | }} | ||
Revision as of 03:39, 4 September 2021
| Wei2012 | |
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| author | Sheng Wei and Nahapetian, Ani and Nelson, Michael and Farinaz Koushanfar and Potkonjak, Miodrag |
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| journal | IEEE Transactions on Information Forensics and Security |
| month | 4 |
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| pages | 765-773 |
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| title | Gate Characterization Using Singular Value Decomposition: Foundations and Applications |
| type | |
| volume | 7 |
| year | 2012 |
| doi | 10.1109/TIFS.2011.2181500 |
| issn | 1556-6021 |
| isbn | |
| url | http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=6112216 |
Email:
farinaz@ucsd.edu
farinaz@ucsd.edu
Address:
Electrical & Computer Engineering
University of California, San Diego
9500 Gilman Drive, MC 0407
Jacobs Hall, Room 6401
La Jolla, CA 92093-0407
Electrical & Computer Engineering
University of California, San Diego
9500 Gilman Drive, MC 0407
Jacobs Hall, Room 6401
La Jolla, CA 92093-0407
Lab Location: EBU1-2514
University of California San Diego
9500 Gilman Dr, La Jolla, CA 92093
University of California San Diego
9500 Gilman Dr, La Jolla, CA 92093