Malware Family Classification System Using Clustering and Similarity Analysis

The objective of this project is to develop a malware family classification system that groups malware samples based on similarity. The system helps security analysts understand malware evolution and identify relationships among different malware variants.

Study malware taxonomy and family classification concepts.

Collect malware datasets belonging to multiple known families.

Extract behavioral or static features from samples.

Implement clustering algorithms such as K-means or hierarchical clustering.

Visualize clusters to interpret family groupings.

Measure similarity scores between malware samples.

Compare clustering results with known malware labels.

Analyze mislabeled or ambiguous samples.

Evaluate clustering accuracy and limitations.

Generate classification reports.

Document insights on malware evolution trends.