Open AccessSoftware Homology Detection With Software Motifs Based on Function-Call Graph
Author(s) -
Peng Wu,
Junfeng Wang,
Bin Tian
Publication year - 2018
Publication title -
ieee access
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.587
H-Index - 127
ISSN - 2169-3536
DOI - 10.1109/access.2018.2803738
Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation
Software homology plays an important role in intellectual property protection, malware analysis, and network attack traceback. Among many methods proposed by researchers, the structure-based method has been proved to have better detection and anti-obfuscation capabilities, but it is inefficiency on space-time complexity and difficult to be applied to large-scale software homology analysis. In this paper, we propose a parallel method to extract function call graph from source codes, and a new software structure information comparison algorithm. The approach transforms function call graph into the corresponding motifs as the features of the software, and calculates homology score by the algorithm which is quick and accurate for large-scale software based on software motifs. According to experiments on large-scale source codes, binary executable files and obfuscated software, the accuracy of homology detection is 90.00% for non-obfuscated software and 80.00% for obfuscated software.
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