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Detection of Similarity in Cross Version Binaries using Raw Bytes
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 1 |
| Year of Publication: 2022 |
| Authors: Nandish M., Mohan H.G. |
10.5120/ijca2022921963
|
Nandish M., Mohan H.G. . Detection of Similarity in Cross Version Binaries using Raw Bytes. International Journal of Computer Applications. 184, 1 ( Mar 2022), 26-29. DOI=10.5120/ijca2022921963
Abstract
Binary code similarity detection (BCSD) technique compares multiple parts of binary code like functions, basic blocks or entire program to check for similarity or differences. Without relying on the source code, binary code analysis allows analysing code. BCSD is used for malware clustering, software theft detection and bug search. Existing techniques for BSCD problem includes Control Flow Graphs (CFG) and deep learning models. Here, a new and simple approach based on single feature to solve the cross-version BCSD problem is proposed. Approach follows initial transformation from functions to vectors and then computes the coefficient value. Proposed approach works on the raw bytes which is implemented and evaluated on a custom dataset having around 23,451 samples. The result shows that the model outperforms all other solutions and the recall of the approach could reach 97.1%.
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