Predicting Malware Attacks using Machine Learning and AutoAI

Mark Sokolov; Nic Herndon

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Paper

Predicting Malware Attacks using Machine Learning and AutoAI

Topics: Classification and Clustering; Ensemble Methods; Machine Learning Methods

In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods ICPRAM - Volume 1, 295-301, 2021

Authors: Mark Sokolov and Nic Herndon

Affiliation: East Carolina University, Greenville, NC, U.S.A.

Keyword(s): Malware, Kaggle, LGBM, AutoAI, Machine Learning.

Abstract: Machine learning is one of the fastest-growing fields and its application to cybersecurity is increasing. In order to protect people from malicious attacks, several machine learning algorithms have been used to predict them. In addition, with the increase of malware threats in our world, a lot of companies use AutoAI to help protect their systems. However, when a dataset is large and sparse, conventional machine learning algorithms and AutoAI don’t generate the best results. In this paper, we propose an Ensemble of Light Gradient Boosted Machines to predict malware attacks on computing systems. We use a dataset provided by Microsoft to show that this proposed method achieves an increase in accuracy over AutoAI.

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Paper citation in several formats:

Sokolov, M. and Herndon, N. (2021). Predicting Malware Attacks using Machine Learning and AutoAI. In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-486-2; ISSN 2184-4313, SciTePress, pages 295-301. DOI: 10.5220/0010264902950301

@conference{icpram21,
author={Mark Sokolov. and Nic Herndon.},
title={Predicting Malware Attacks using Machine Learning and AutoAI},
booktitle={Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2021},
pages={295-301},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010264902950301},
isbn={978-989-758-486-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Predicting Malware Attacks using Machine Learning and AutoAI
SN - 978-989-758-486-2
IS - 2184-4313
AU - Sokolov, M.
AU - Herndon, N.
PY - 2021
SP - 295
EP - 301
DO - 10.5220/0010264902950301
PB - SciTePress