Name
Title | ||
|---|---|---|
Deep-evasion: Turn deep neural network into evasive self-contained cyber-physical malware: poster |
Abstract | ||
|---|---|---|
Deep Neural Network (DNN) based intelligent Cyber-Physical Systems (CPS) are becoming more and more popular across all aspects of our lives. Unfortunately, such a promising trend implies a dangerous feature that allows code to be mixed with data in DNN models and triggered by a targeted physical object without harming the DNN inference accuracy. In this work, we investigate such an emerging attack, namely "Deep-Evasion", turning DNN into evasive self-contained malware on CPS. We prototype "Deep-Evasion" on Nvidia Jetson TX2 embedded device and demonstrate a Denial-of-Service (DoS) attack as our proof of concept. Experimental results show "Deep-Evasion" is feasible, reliable and scalable on CPS.
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Year | DOI | Venue |
|---|---|---|
2019 | 10.1145/3317549.3326311 | Proceedings of the 12th Conference on Security and Privacy in Wireless and Mobile Networks |
Field | DocType | ISBN |
Computer science,Computer security,Cyber-physical system,Malware,Artificial neural network | Conference | 978-1-4503-6726-4 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
2 | ||