IMPROVING BINARY DIFFING SPEED AND ACCURACY USING COMMUNITY DETECTION AND LOCALITY-SENSITIVE HASHING 

Abstract:

Binary diffing, the process of identifying differences between two binary files, plays a crucial role in various domains such as software security, reverse engineering, and software version control. However, the increasing complexity and size of binary files have posed significant challenges to achieving efficient and accurate diffing.

This research proposes a novel approach to enhance binary diffing speed and accuracy by leveraging community detection and locality-sensitive hashing techniques. The proposed method aims to address the limitations of existing diffing approaches, including high computational complexity and low detection accuracy.

To improve speed, the method utilizes locality-sensitive hashing (LSH), a technique that maps similar items to the same or nearby hash buckets. By employing LSH, the binary files can be efficiently indexed, enabling faster comparison and identification of differences.

To enhance accuracy, the proposed approach incorporates community detection algorithms. Community detection algorithms identify groups of related data within a network or dataset. In the context of binary diffing, these algorithms can help identify clusters of similar code segments or features that are likely to exhibit differences between the files. By focusing the diffing process on these specific regions, the accuracy of the diffing results can be significantly improved.

The experimental evaluation of the proposed approach demonstrates promising results. The combination of locality-sensitive hashing and community detection algorithms reduces the computational complexity of the diffing process while maintaining high accuracy. The experiments include a diverse set of binary files, and the results show significant improvements in both speed and accuracy compared to existing diffing techniques.

In conclusion, this research presents a novel approach to improve binary diffing speed and accuracy by leveraging community detection and locality-sensitive hashing. The proposed method addresses the challenges posed by complex and large binary files and provides a more efficient and accurate solution for identifying differences between binary files. The results of this research have the potential to benefit various domains, including software security, reverse engineering, and software version control.

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