Enhancing Malware Analysis Using Data Visualization Through Shared Code and Attribute Analysis

Malware analysis is a crucial area of cybersecurity, focusing on identifying, categorizing, and studying malicious software to prevent it from posing a threat to computer systems. This study introduces a new approach to malware analysis by integrating two methods, Shared Code Analysis (SCA) and Shared Attribute Analysis (SAA). The SCA component searches for similarities in code within multiple malware samples using the MinHash techniques. The SAA component uses the Jaccard indexing technique to analyze and approximate similarity based on malware samples’ attributes. The visualization is implemented using NetworkX with Graphviz, where nodes represent individual malware samples and edges represent close relations. The visualization is provided as a PNG file, demonstrating the grouping of related malware samples according to their attributes. A real-life case based on malware samples from GitHub repositories illustrates the application of the proposed methodology. The integration of the Jaccard Index and MinHash provides an extensive method for covering the relationships among malware and enhancing detection and categorization steps. The proposed approach not only provides a better understanding of code patterns, but it also helps visualize attribute-based maximum similarity, making it a valuable tool for researchers and cybersecurity professionals.