SMOKE: Simulink Model Obfuscator Keeping Structure

Simulink is extensively used across various industries to model and simulate cyber-physical systems. Most industry-built models contain sensitive intellectual property, which prevents companies from sharing models with interested third parties, such as researchers. Initiatives to replace industry-built models with open-source alternatives exist, however they offer only limited remedy. In this work, we offer a novel approach: a Simulink obfuscation tool named SMOKE, designed to selectively protect intellectual property in models. This allows companies to share relevant parts of their models with researchers or other third parties, while safeguarding all sensitive information. We evaluated the tool on an extensive set of open-source models and found it successfully removes sensitive components, while preserving model structure. A video demonstration of SMOKE is available online at https://youtu.be/2KI6HGHrJ20.

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Publication Details

Published
2024-09-22
DOI
https://doi.org/10.1145/3652620.3687788
Primary Topic
Advanced Malware Detection Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

SMOKE: Simulink Model Obfuscator Keeping Structure

Alexander Boll, Timo Kehrer, Michael Goedicke
Advanced Malware Detection Techniques
article

SMOKE: Simulink Model Obfuscator Keeping Structure

Alexander Boll, Timo Kehrer, Michael Goedicke
article en

Abstract

Simulink is extensively used across various industries to model and simulate cyber-physical systems. Most industry-built models contain sensitive intellectual property, which prevents companies from sharing models with interested third parties, such as researchers. Initiatives to replace industry-built models with open-source alternatives exist, however they offer only limited remedy. In this work, we offer a novel approach: a Simulink obfuscation tool named SMOKE, designed to selectively protect intellectual property in models. This allows companies to share relevant parts of their models with researchers or other third parties, while safeguarding all sensitive information. We evaluated the tool on an extensive set of open-source models and found it successfully removes sensitive components, while preserving model structure. A video demonstration of SMOKE is available online at https://youtu.be/2KI6HGHrJ20.

University of Bern (CH), University of Duisburg-Essen (DE)
Deutsche Forschungsgemeinschaft
Openalex Percentile: Top 79%
Advanced Malware Detection Techniques
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