Hiding in Plain Sight? On the Efficacy of Power Side Channel-Based Control Flow Monitoring
Yi Han, Matthew Chan, Zahra Aref, Nils Ole Tippenhauer, Saman A. Zonouz
摘要
Physical side-channel monitoring leverages the physical phenomena produced by a microcontroller (e.g. power consumption or electromagnetic radiation) to monitor program execution for malicious behavior. As such, it offers a promising intrusion detection solution for resource-constrained embedded systems, which are incompatible with conventional security measures. This method is especially relevant in safety and security-critical embedded systems such as in industrial control systems. Side-channel monitoring poses unique challenges for would-be attackers, such as: (1) limiting attack vectors by being physically isolated from the monitored system, (2) monitoring immutable physical side channels with uninterpretable data-driven models, and (3) being specifically trained for the architectures and programs on which they are applied to. As a result, physical side-channel monitors are conventionally believed to provide a high level of security. In this paper, we propose a novel attack to illustrate that, despite the many barriers to attack that side-channel monitoring systems create, they are still vulnerable to adversarial attacks. We present a method for crafting functional malware such that, when injected into a side-channel-monitored system, the detector is not triggered. Our experiments reveal that this attack is robust across detector models and hardware implementations. We evaluate our attack on the popular ARM microcontroller platform on several representative programs, demonstrating the feasibility of such an attack and highlighting the need for further research into side-channel monitors.
问问这篇 Paper
智能体会读完全文。
Lune 把这篇 Paper 索引到了每一个公式,引用它的顶会 Paper 也一样。你提问,回答直接引用原文。
引用它的顶会 Paper1
问问它们各自怎么用它它引用的顶会 Paper19
- SOK: (State of) The Art of War: Offensive Techniques in Binary AnalysisYan Shoshitaishvili, Ruoyu Wang, Christopher Salls, Nick Stephens 等S&P 2016 · 被引用 1,085 次
- Driller: Augmenting Fuzzing Through Selective Symbolic ExecutionNick Stephens, John Grosen, Christopher Salls, Andrew Dutcher 等NDSS 2016 · 被引用 1,021 次
- AI2: Safety and Robustness Certification of Neural Networks with Abstract InterpretationTimon Gehr, Matthew Mirman, Dana Drachsler-Cohen, Petar Tsankov 等S&P 2018 · 被引用 987 次
- Kitsune: An Ensemble of Autoencoders for Online Network Intrusion DetectionYisroel Mirsky, Tomer Doitshman, Yuval Elovici, Asaf ShabtaiNDSS 2018 · 被引用 945 次
- Formal Security Analysis of Neural Networks using Symbolic IntervalsShiqi Wang, Kexin Pei, Justin Whitehouse, Junfeng Yang 等USENIX Security 2018 · 被引用 523 次
相关 Paper
- On Code Execution Tracking via Power Side-ChannelYannan Liu, Lingxiao Wei, Zhe Zhou, Kehuan Zhang 等CCS 2016 · 被引用 84 次
- Truth Will Out: Departure-Based Process-Level Detection of Stealthy Attacks on Control SystemsWissam Aoudi, Mikel Iturbe, Magnus AlmgrenCCS 2018 · 被引用 110 次
- Detection of Electromagnetic Interference Attacks on Sensor SystemsYouqian Zhang, Kasper RasmussenS&P 2020 · 被引用 68 次
- Runtime Trust Evaluation and Hardware Trojan Detection Using On-Chip EM SensorsJiaji He, Xiaolong Guo, Haocheng Ma, Yanjiang Liu 等DAC 2020 · 被引用 25 次
- Watch Me, but Don't Touch Me! Contactless Control Flow Monitoring via Electromagnetic EmanationsYi Han, Sriharsha Etigowni, Hua Liu, Saman A. Zonouz 等CCS 2017 · 被引用 110 次
