Source URL: https://edmcman.github.io/blog/2024-01-11–fuzzy-hashing-for-code-comparisons/
Source: Hacker News
Title: The performance of hashing for similar function detection
Feedly Summary: Comments
AI Summary and Description: Yes
Summary: The text delves deeply into malware reverse engineering and explores advanced hashing techniques like PIC hashing and fuzzy hashing. It highlights the significant challenges faced in identifying equivalent functions across different software versions, particularly in compiled code, emphasizing the accuracy and limitations of these methods. This discussion is particularly relevant for security professionals focused on malware detection and reverse engineering.
Detailed Description: The content presents a comprehensive analysis of code comparison techniques important in malware reverse engineering, specifically through the lens of hashing methods. Below are key points addressed in the text:
– **Reverse Engineering Malware**: Discusses the challenges of identifying malware variants, as slight modifications by authors can render re-analysis tedious without efficient techniques.
– **Code Comparison Techniques**:
– **Exact Hashing**: Explains the limitations of exact hashing, which can fail when small changes occur in the code’s structure.
– **PIC Hashing**: Introduces Position Independent Code (PIC) hashing, which allows for the comparison of functions irrespective of their memory addresses, thus addressing some shortcomings of exact hashing.
– **Fuzzy Hashing**: Discusses fuzzy hashing techniques that assess the similarity between codes, which can yield better matches when codes are not identical but related.
– **Experiments and Findings**:
– The text provides a series of experiments comparing OpenSSL versions compiled with different configurations. Results include precision, recall, and F1 scores for each method:
– **Metrics**: Key performance indicators (accuracy, precision, recall, F1 score) are emphasized to determine the effectiveness of each hashing technique in identifying equivalent functions.
– **Confusion Matrices**: Utilized to evaluate the performance of PIC and fuzzy hashing techniques against different test conditions.
– **Limitations**: The analysis underscores that while PIC hashing is efficient and provides high precision, it can struggle with significant changes in compilations. Fuzzy hashing methods might offer a broader view but require optimal thresholds that can be challenging to determine in practice.
– **Future Considerations**: The text concludes with a look towards future approaches, such as using semantic information in function comparisons or leveraging neural networks for improved similarity assessments.
Overall, this comprehensive exploration of malware reverse engineering methodologies is invaluable for professionals focused on enhancing security protocols through effective code analysis and comparison. The discussion not only informs but invites further inquiry into adopting innovative strategies that bridge current gaps in code comparison and malware detection.