A Method to Evaluate CFG Comparison Algorithms

Chan, Patrick; Collberg, Christian

doi:10.1109/qsic.2014.28

Cited by 19 publications

(6 citation statements)

References 22 publications

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“…Since each concrete similarity algorithm exploits graph characteristics for a field-specific problem, we selected algorithms which have already shown to be effective for software security research. Algorithms used for graph similarity problems in the software context are close to our hardware applications (e.g., Trojan detection and reverse engineering), see Chan et al [18] or Hu et al [9].…”

Section: Discussionmentioning

confidence: 99%

“…Third, graph similarity is computed ( -). Note that the original algorithm computed an approximation of the graph edit distance and not a similarity score, Chan et al [18] defined a formula to compute this value for a given edit distance . The computational complexity of the graph edit distance approximation is OððjV 1 j þ jV 2 jÞ 6 Þ since the Hungarian method can be implemented with OðjV j 3 Þ.…”

Section: Algorithm 1 Graph Edit Distance Approximationmentioning

confidence: 99%

“…To compute the edit d istance 4 , we use distinct strategies for all 4 different parameter possibilities, see Table 1. Equation ( 1) states the original edit distance cost formula by Chan et al [18]…”

Section: Algorithm 1 Graph Edit Distance Approximationmentioning

confidence: 99%

“…Note that a value in range ½0:1 À 0:3 resulted in higher false negative rate and smaller false positive rate, values in range ½0:7 À 0:9 caused a higher false positive rate. Second, the similarity matrix is iteratively updated until each element in the temporary matrix ðtmp ij Þ is not larger than some chosen precision 9 , i.e., ¼ 10 À4 [18]. In each iteration ( 10 -), the new similarity matrix ðsim ij Þ is determined as follows: for vertex pair ðv i ; v j Þ the optimal vertex matching is computed with the Hungarian method 19 .…”

Section: Neighbour Matchingmentioning

confidence: 99%

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Graph Similarity and its Applications to Hardware Security

Fyrbiak

Wallat

Reinhard

et al. 2020

IEEE Trans. Comput.

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Hardware reverse engineering is a powerful and universal tool for both security engineers and adversaries. From a defensive perspective, it allows for detection of intellectual property infringements and hardware Trojans, while it simultaneously can be used for product piracy and malicious circuit manipulations. From a designer's perspective, it is crucial to have an estimate of the costs associated with reverse engineering, yet little is known about this, especially when dealing with obfuscated hardware. The contribution at hand provides new insights into this problem, based on algorithms with sound mathematical underpinnings. Our contributions are threefold: First, we present the graph similarity problem for automating hardware reverse engineering. To this end, we improve several state-of-the-art graph similarity heuristics with optimizations tailored to the hardware context. Second, we propose a novel algorithm based on multiresolutional spectral analysis of adjacency matrices. Third, in three extensively evaluated case studies, namely (1) gate-level netlist reverse engineering, (2) hardware Trojan detection, and (3) assessment of hardware obfuscation, we demonstrate the practical nature of graph similarity algorithms.

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Section: Discussionmentioning

confidence: 99%

Section: Algorithm 1 Graph Edit Distance Approximationmentioning

confidence: 99%

Section: Algorithm 1 Graph Edit Distance Approximationmentioning

confidence: 99%

Section: Neighbour Matchingmentioning

confidence: 99%

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Graph Similarity and its Applications to Hardware Security

Fyrbiak

Wallat

Reinhard

et al. 2020

IEEE Trans. Comput.

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“…Под сопоставлением здесь понимается присвоение этим узлам одинаковых меток, уникальных в рамках каждого графа. Некоторые другие подходы к сравнению графов рассматриваются в [31]. Там же предлагается методология для оценки различных способов сравнения графов потока управления.…”

Section: похожесть графов потока управленияunclassified

On the Automatic Analysis of the Practical Resistance of Obfusting Transformations

Borisov

Косолапов

2019

Model. anal. inf. sist.

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A method is developed for assessing the practical persistence of obfuscating transformations of programs based on the calculation of the similarity index for the original, obfuscated and deobfuscated programs. Candidates are proposed for similarity indices, which are based on such program characteristics as the control flow graph, symbolic execution time and degree of coverage for symbolic execution. The control flow graph is considered as the basis for building other candidates for program similarity indicators. On its basis, a new candidate is proposed for the similarity index, which, when calculated, finds the Hamming distance between the adjacency matrices of control flow graphs of compared programs. A scheme for estimating (analyzing) the persistence of obfuscating transformations is constructed, according to which for the original, obfuscated and deobfuscated programs, the characteristics of these programs are calculated and compared in accordance with the chosen comparison model. The developed scheme, in particular, is suitable for comparing programs based on similarity indices. This paper develops and implements one of the key units of the constructed scheme - a block for obtaining program characteristics compiled for the x86/x86 64 architecture. The developed unit allow to find the control flow graph, the time for symbolic execution and the degree of coverage for symbolic execution. Some results of work of the constructed block are given.

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