Comparing Machine Learning Techniques for Malware Detection

Joanna Moubarak, Tony Feghali

2020

Abstract

Cyberattacks and the use of malware are more and more omnipresent nowadays. Targets are as varied as states or publicly traded companies. Malware analysis has become a very important activity in the management of computer security incidents. Organizations are often faced with suspicious files captured through their antiviral and security monitoring systems, or during forensics analysis. Most solutions funnel out suspicious files through multiple tactics correlating static and dynamic techniques in order to detect malware. However, these mechanisms have many practical limitations giving rise to a new research track. The aim of this paper is to tackle the use of machine learning algorithms to analyze malware and expose how data science is used to detect malware. Training systems to find attacks allows to develop better protection tools, capable of detecting unprecedented campaigns. This study reveals that many models can be employed to evaluate their detectability. Our demonstration results illustrates the possibility to analyze malware leveraging several machine learning (ML) algorithms comparing them.

Paper Citation

in Harvard Style

Moubarak J. and Feghali T. (2020). Comparing Machine Learning Techniques for Malware Detection. In Proceedings of the 6th International Conference on Information Systems Security and Privacy - Volume 1: ForSE, ISBN 978-989-758-399-5, pages 844-851. DOI: 10.5220/0009373708440851

in Bibtex Style

@conference{forse20,
author={Joanna Moubarak and Tony Feghali},
title={Comparing Machine Learning Techniques for Malware Detection},
booktitle={Proceedings of the 6th International Conference on Information Systems Security and Privacy - Volume 1: ForSE,},
year={2020},
pages={844-851},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009373708440851},
isbn={978-989-758-399-5},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 6th International Conference on Information Systems Security and Privacy - Volume 1: ForSE,
TI - Comparing Machine Learning Techniques for Malware Detection
SN - 978-989-758-399-5
AU - Moubarak J.
AU - Feghali T.
PY - 2020
SP - 844
EP - 851
DO - 10.5220/0009373708440851