Online Synthesis of Adaptive Side-Channel Attacks Based On Noisy Observations

Online Synthesis of Adaptive Side-Channel Attacks Based On Noisy Observations
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基于噪声观测的自适应侧信道攻击的在线合成

DOI:
10.1109/eurosp.2018.00029
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发表时间:
2018
期刊:
2018 IEEE European Symposium on Security and Privacy (EuroS&P)
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--
通讯作者:
T. Bultan
T. Bultan
中科院分区:
--
文献类型:
--
作者:
Lucas Bang;Nicolás Rosner;T. Bultan

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我们提出了一种自动化的技术,用于合成自适应攻击,以从通过侧通道泄漏秘密数据的程序函数中提取信息。我们动态合成攻击步骤,并考虑嘈杂的程序环境。我们的方法包括一个离线分析阶段,使用符号执行,证人生成,和配置文件构建一个噪声模型。在我们的在线攻击合成阶段,我们使用加权模型计数和数值优化来自动合成攻击输入。我们的实验评估我们的方法的有效性DARPA的基准程序创建测试侧通道分析技术。
We present an automated technique for synthesizing adaptive attacks to extract information from program functions that leak secret data through a side channel. We synthesize attack steps dynamically and consider noisy program environments. Our approach consists of an offline profiling phase using symbolic execution, witness generation, and profiling to construct a noise model. During our online attack synthesis phase, we use weighted model counting and numeric optimization to automatically synthesize attack inputs. We experimentally evaluate the effectiveness of our approach on DARPA benchmark programs created for testing side-channel analysis techniques.
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发表时间: 2016-08
期刊: 2016 IEEE 29th Computer Security Foundations Symposium (CSF)
影响因子: --
作者:
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