Scalable Hardware Trojan Diagnosis
Scalable Hardware Trojan Diagnosis
复制标题
可扩展的硬件木马诊断
DOI：
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复制
发表时间：
2012
影响因子：
2.8
通讯作者：
M. Potkonjak
中科院分区：
文献类型：
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作者：
Sheng Wei;M. Potkonjak
硬件木马（HTs）对现代和未来的集成电路（IC）构成了重大威胁。由于高温超导体的多样性和集成电路设计中固有的工艺偏差，高温超导体的检测和定位是一个挑战。已经提出了几种方法来解决这个问题，但它们要么无法检测各种类型的HT，要么无法处理非常大的电路。我们已经开发了一个可扩展的HT检测和诊断方法，使用分割和门级表征（GLC）。我们通过测量一组不同输入向量的总体泄漏电流来确保检测任意恶意电路。为了解决可扩展性问题，我们采用了一种分割方法，将大电路分成小的子电路，使用输入向量选择。我们开发了一个段选择模型的属性段和GLC精度的影响。模型参数由GLC过程的采样数据标定。基于所选择的段，我们能够检测和诊断HT通过跟踪门级泄漏功率。我们评估我们的方法在几个ISCAS 85/ISCAS 89/ITC 99基准。仿真结果表明，我们的方法是能够准确地检测和诊断HT的大型电路。
Hardware Trojans (HTs) pose a significant threat to the modern and pending integrated circuit (IC). Due to the diversity of HTs and intrinsic process variation (PV) in IC design, detecting and locating HTs is challenging. Several approaches have been proposed to address the problem, but they are either incapable of detecting various types of HTs or unable to handle very large circuits. We have developed a scalable HT detection and diagnosis approach that uses segmentation and gate level characterization (GLC). We ensure the detection of arbitrary malicious circuitry by measuring the overall leakage current for a set of different input vectors. In order to address the scalability issue, we employ a segmentation method that divides the large circuit into small sub-circuits using input vector selection. We develop a segment selection model in terms of properties of segments and their effects on GLC accuracy. The model parameters are calibrated by sampled data from the GLC process. Based on the selected segments we are able to detect and diagnose HTs by tracing gate level leakage power. We evaluate our approach on several ISCAS85/ISCAS89/ITC99 benchmarks. The simulation results show that our approach is capable of detecting and diagnosing HTs accurately on large circuits.