Adaptable and Divergent Synthetic Benchmark Generation for Hardware Security

Benchmarking can drive the development of technologies by facilitating standardization of features for comparison of different methods. While hardware security has seen an exponential growth in innovation throughout the last decade, the lack of sufficient benchmarks for data-driven analysis is promi...

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Vydané v:Digest of technical papers - IEEE/ACM International Conference on Computer-Aided Design s. 1 - 9
Hlavní autori: Amir, Sarah, Forte, Domenic
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: Association on Computer Machinery 02.11.2020
Predmet:
Benchmark testing
circuit optimisation
combinational circuits
data-driven analysis
data-driven hardware security
exponential growth
feature space
Hardware
hardware security
input reference benchmarks
invasive software
linear optimization
linear programming
Logic gates
machine learning
Optimization
quantifiable structural features
Security
security evaluation
statistical analysis
Synthetic benchmark
synthetic benchmark generation process flow
synthetic combinational benchmarks
Trojan horses
user input constraints
VLSI
VLSI benchmarks
ISSN:1558-2434
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Popis
Shrnutí:Benchmarking can drive the development of technologies by facilitating standardization of features for comparison of different methods. While hardware security has seen an exponential growth in innovation throughout the last decade, the lack of sufficient benchmarks for data-driven analysis is prominent. Researchers must currently rely on decades-old VLSI benchmarks, which in most cases were not designed with security evaluation in mind. Considering the present day computational power, these benchmarks lack in both quality and quantity for usage in hardware security topics such as obfuscation and hardware Trojans. Many advanced techniques, like statistical analysis and machine learning, require a large number of samples in order to sufficiently examine the feature space. In an attempt to resolve this issue, we have developed the first synthetic benchmark generation process flow. This paper describes our novel technique that utilizes linear optimization to generate an endless number of synthetic combinational benchmarks that are adaptable to user input constraints and divergent in quantifiable structural features from input reference benchmarks. Thus, our framework offers customization for generating richer and more challenging benchmarks for data-driven hardware security. Through experimentation, we verify that our benchmarks offers more structural variation than the current benchmark suites.
ISSN:1558-2434
DOI:10.1145/3400302.3415648