Distance Metric Learning using Particle Swarm Optimization to Improve Static Malware Detection

Martin Jureček, Róbert Lórencz

Abstract

Distance metric learning is concerned with finding appropriate parameters of distance function with respect to a particular task. In this work, we present a malware detection system based on static analysis. We use k-nearest neighbors (KNN) classifier with weighted heterogeneous distance function that can handle nominal and numeric features extracted from portable executable file format. Our proposed approach attempts to specify the weights of the features using particle swarm optimization algorithm. The experimental results indicate that KNN with the weighted distance function improves classification accuracy significantly.

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Paper Citation


in Harvard Style

Jureček M. and Lórencz R. (2020). Distance Metric Learning using Particle Swarm Optimization to Improve Static Malware Detection.In Proceedings of the 6th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP, ISBN 978-989-758-399-5, pages 725-732. DOI: 10.5220/0009180807250732


in Bibtex Style

@conference{icissp20,
author={Martin Jureček and Róbert Lórencz},
title={Distance Metric Learning using Particle Swarm Optimization to Improve Static Malware Detection},
booktitle={Proceedings of the 6th International Conference on Information Systems Security and Privacy - Volume 1: ICISSP,},
year={2020},
pages={725-732},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0009180807250732},
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: ICISSP,
TI - Distance Metric Learning using Particle Swarm Optimization to Improve Static Malware Detection
SN - 978-989-758-399-5
AU - Jureček M.
AU - Lórencz R.
PY - 2020
SP - 725
EP - 732
DO - 10.5220/0009180807250732