Deep-Evasion: Turn Deep Neural Network into Evasive Self-Contained Cyber-Physical Malware

被引：4

作者：

Liu, Tao ^{[1
]}

Wen, Wujie ^{[1
]}

机构：

[1] Florida Int Univ, Miami, FL 33199 USA

来源：

PROCEEDINGS OF THE 2019 CONFERENCE ON SECURITY AND PRIVACY IN WIRELESS AND MOBILE NETWORKS (WISEC '19) | 2019年

关键词：

D O I：

10.1145/3317549.3326311

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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.

引用

收藏

页码：320 / 321

页数：2

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