Empirical Analysis for Investigating the Effect of Machine Learning Techniques on Malware Prediction

Sanidhya Vijayvargiya, Lov Kumar, Lalita Murthy, Sanjay Misra, Aneesh Krishna, Srinivas Padmanabhuni

2023

Abstract

Malware is used to attack computer systems and network infrastructure. Therefore, classifying malware is essential for stopping hostile attacks. In the after-effects of COVID-19, the virtual presence of individuals has greatly increased. From money transactions to personal information, everything is shared and stored in cyberspace. This has led to increased and more innovative malware attacks. Advanced packing and obfuscation methods are being used by malware variants to get access to private information for profit. There is an urgent need for better software security. In this paper, we identify the best ML techniques that can be used in combination with various ML and ensemble classifiers for malware classification. The goal of this work is to identify the ideal ML pipeline for detecting the family of malware. Imbalanced datasets and a lack of feature selection have plagued many previous works. The best tools for describing malware activity are application programming interfaces (APIs). However, creating API call attributes for classification algorithms to achieve high accuracy is challenging. The dataset used to validate the proposed method includes API call count histogram features extracted by dynamic analysis. The experimental results demonstrate that the proposed ML pipeline may effectively and accurately categorize malware, producing state-of-the-art results.

Paper Citation

in Harvard Style

Vijayvargiya S., Kumar L., Murthy L., Misra S., Krishna A. and Padmanabhuni S. (2023). Empirical Analysis for Investigating the Effect of Machine Learning Techniques on Malware Prediction. In Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE, ISBN 978-989-758-647-7, SciTePress, pages 453-460. DOI: 10.5220/0011858200003464

in Bibtex Style

@conference{enase23,
author={Sanidhya Vijayvargiya and Lov Kumar and Lalita Murthy and Sanjay Misra and Aneesh Krishna and Srinivas Padmanabhuni},
title={Empirical Analysis for Investigating the Effect of Machine Learning Techniques on Malware Prediction},
booktitle={Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,},
year={2023},
pages={453-460},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0011858200003464},
isbn={978-989-758-647-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 18th International Conference on Evaluation of Novel Approaches to Software Engineering - Volume 1: ENASE,
TI - Empirical Analysis for Investigating the Effect of Machine Learning Techniques on Malware Prediction
SN - 978-989-758-647-7
AU - Vijayvargiya S.
AU - Kumar L.
AU - Murthy L.
AU - Misra S.
AU - Krishna A.
AU - Padmanabhuni S.
PY - 2023
SP - 453
EP - 460
DO - 10.5220/0011858200003464
PB - SciTePress