Analysis of sequential feature engineering and statistical features for malware behavior discovery

Bachelor thesis (2021)

Authors

M.P. Epifanov Electrical Engineering, Mathematics and Computer Science

Contributors

S.E. Verwer Cyber Security - EEMCS (supervisor 1)
A. Nadeem Cyber Security - EEMCS (supervisor 1)
M.A. Migut Computer Science & Engineering-Teaching Team - EEMCS (coach)
Faculty
Electrical Engineering, Mathematics and Computer Science
Copyright: © 2021 Mikhail Epifanov
Malpaca Network security Unsupervised Machine Learning
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Published Date

01-07-2021

Language

English

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Faculty

Electrical Engineering, Mathematics and Computer Science

Abstract

Malware Packet-sequence Clustering and Analysis (MalPaCA) is a unsupervised clustering application for malicious network behavior, it currently uses solely sequential features to characterize network behavior. In this paper an extensive comparison between those features and statistical features is performed. During the comparison a better clustering performance achievable with statistical features for longer connection sequences is shown and advice on which features can be added to MalPaCA.