An Inclusive Report on Robust Malware Detection and Analysis for Cross-Version Binary Code Optimizations

Abstract

Numerous practices exist for binary code similarity detection (BCSD), such as Control Flow Graph, Semantics Scrutiny, Code Obfuscation, Malware Detection and Analysis, vulnerability search, etc. On the basis of professional knowledge, existing solutions often compare particular syntactic aspects retrieved from binary code. They either have substantial performance overheads or have inaccurate detection. Furthermore, there aren't many tools available for comparing cross-version binaries, which may differ not only in programming with proper syntax but also marginally in semantics. This Binary code similarity detection is existing for past 10 years, but this research area is not yet systematically analysed. The paper presents a comprehensive analysis on existing Cross-version Binary Code Optimization techniques on four characteristics: 1. Structural analysis, 2. Semantic Analysis, 3. Syntactic Analysis, 4. Validation Metrics.  It helps the researchers to best select the suitable tool for their necessary implementation on binary code analysis. Furthermore, this paper presents scope of the area along with future directions of the research.