Online Clustering of Known and Emerging Malware Families

TL;DR

This paper presents a machine learning model for online clustering of malware samples, enabling rapid identification of known and emerging malware families with high cluster purity, based on static analysis of Windows executables.

Contribution

It introduces a novel online clustering approach combining weighted k-NN and online k-means for malware family classification, improving response time and cluster purity.

Findings

Cluster purity ranges from 90.20% to 93.34%.

Effective separation of known and emerging malware families.

Speeds up malware analysis process.

Abstract

Malware attacks have become significantly more frequent and sophisticated in recent years. Therefore, malware detection and classification are critical components of information security. Due to the large amount of malware samples available, it is essential to categorize malware samples according to their malicious characteristics. Clustering algorithms are thus becoming more widely used in computer security to analyze the behavior of malware variants and discover new malware families. Online clustering algorithms help us to understand malware behavior and produce a quicker response to new threats. This paper introduces a novel machine learning-based model for the online clustering of malicious samples into malware families. Streaming data is divided according to the clustering decision rule into samples from known and new emerging malware families. The streaming data is classified using the weighted k-nearest neighbor classifier into known families, and the online k-means algorithm clusters the remaining streaming data and achieves a purity of clusters from 90.20% for four clusters to 93.34% for ten clusters. This work is based on static analysis of portable executable files for the Windows operating system. Experimental results indicate that the proposed online clustering model can create high-purity clusters corresponding to malware families. This allows malware analysts to receive similar malware samples, speeding up their analysis.