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ObfusX: Routing obfuscation with explanatory analysis of a machine learning attack.
- Source :
-
Integration: The VLSI Journal . Mar2023, Vol. 89, p47-55. 9p. - Publication Year :
- 2023
-
Abstract
- This is the first work that incorporates recent advancements in "explainability" of machine learning (ML) to build a routing obfuscator called ObfusX. We adopt a recent metric—the SHAP value—which explains to what extent each layout feature can reveal each unknown connection for a recent ML-based split manufacturing attack model. The unique benefits of SHAP-based analysis include the ability to identify the best candidates for obfuscation, together with the dominant layout features which make them vulnerable. As a result, ObfusX can achieve better hit rate (97% lower) while perturbing significantly fewer nets when obfuscating using a via perturbation scheme, compared to prior work. When imposing the same wirelength limit using a wire lifting scheme, ObfusX performs significantly better in performance metrics (e.g., 2.2 times more reduction on average in percentage of netlist recovery). [ABSTRACT FROM AUTHOR]
- Subjects :
- *ARTIFICIAL intelligence
*MACHINE learning
Subjects
Details
- Language :
- English
- ISSN :
- 01679260
- Volume :
- 89
- Database :
- Academic Search Index
- Journal :
- Integration: The VLSI Journal
- Publication Type :
- Academic Journal
- Accession number :
- 161303355
- Full Text :
- https://doi.org/10.1016/j.vlsi.2022.10.013