Similarity Metric Method for Binary Basic Blocks of Cross-Instruction Set Architecture
Xiaochuan Zhang, Wenjie Sun, Jianmin Pang, Fudong Liu, Zhen Ma
Abstract
Basic block similarity analysis is a fundamental technique in many machine learning-based binary program analysis methods. The key to basic block similarity analysis is mapping the semantic information of the basic block to a fixeddimension vector, which is the so-called basic block embedding. However, existing solutions to basic block embedding suffer from two major limitations. 1) The basic block embedding contains limited semantic information; 2) they are only applicable to a single instruction set architecture (ISA).
Topics & Concepts
Computer scienceBinary numberSet (abstract data type)Similarity (geometry)Metric (unit)ArchitectureTheoretical computer scienceArtificial intelligenceMathematicsArithmeticProgramming languageEngineeringOperations managementVisual artsImage (mathematics)ArtGraph Theory and AlgorithmsText and Document Classification TechnologiesWeb Data Mining and Analysis