Online Clustering of Known and Emerging Malware Families

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21240%2F25%3A00385454" target="_blank" >RIV/68407700:21240/25:00385454 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/978-3-031-83157-7_2" target="_blank" >https://doi.org/10.1007/978-3-031-83157-7_2</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-83157-7_2" target="_blank" >10.1007/978-3-031-83157-7_2</a>

Result language

angličtina

Original language name

Online Clustering of Known and Emerging Malware Families

Original language description

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.

Czech name

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Czech description

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Type

C - Chapter in a specialist book

CEP classification

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OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/EF16_019%2F0000765" target="_blank" >EF16_019/0000765: Research Center for Informatics</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2025

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Book/collection name

Machine Learning, Deep Learning and AI for Cybersecurity

ISBN

978-3-031-83156-0

Number of pages of the result

23

Pages from-to

37-59

Number of pages of the book

642

Publisher name

Springer Nature Switzerland AG

Place of publication

Basel

UT code for WoS chapter

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