Survey Organizes Malware Clustering Techniques
Original title: Malware Clustering in the Wild: Taxonomy, Challenges, and Evaluation Metrics
Survey organizes prior malware clustering research into seven categories and highlights AI-driven methods for large-scale reproducible analysis.
As malware grows in volume and sophistication clustering has become critical for analyzing large corpora to discover relationships and patterns. Existing research remains fragmented with no unified perspective. The survey organizes previous work into centroid-based, hierarchical-based, density-based, distribution-based, mixed-type, fuzzy, and AI-driven approaches.
- Organizes malware clustering into seven categories
- Focus on publicly available malware datasets and features
- AI-driven methods for future reproducible analysis
- Survey clarifies current landscape and gaps
From the original article
1 min read at arxiv.orgAs malware continues to grow in volume, diversity, and sophistication, clustering has emerged as a critical technique for analyzing large malware corpora. Clustering enables the discovery of structural relationships, behavioral patterns, and evolutionary trends, and is widely used to group unknown samples, infer malware family boundaries, and identify novel variants.
By Raguvir S · arxiv.org
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