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A Malware Detection Method Based on Machine Learning and Ensemble of Regression Trees

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Ang LiAuthors Info & Claims

ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information Systems

Article No.: 280, Pages 1 - 6

https://doi.org/10.1145/3469213.3470713

Published: 18 August 2021 Publication History

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Abstract

In the context of the current large number of malicious codes, the detection and protection of malicious codes is particularly important. In recent years, a method of using deep learning to detect malicious code has emerged. Thus, in this paper, we propose a new detection method that converts binary files of malicious code into decimal arrays and use 1-D CNN to perform classification and recognition. Aiming at the imbalance in the number of code families, we choose xgboost, which performs well in the classification prediction competition. We conduct experiments on 9,458 malware samples from 25 different malware families in the Vision Research Lab. The experimental results show that our classification prediction reaches 97% accuracy.

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Maniriho PMahmood AChowdhury M(2024)A systematic literature review on Windows malware detectionJournal of Systems and Software10.1016/j.jss.2023.111921209:COnline publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1016/j.jss.2023.111921
Srinu IVidyarthi D(2023)Classification of Malware Using Deep Learning: A Study2023 IEEE International Carnahan Conference on Security Technology (ICCST)10.1109/ICCST59048.2023.10474230(1-6)Online publication date: 11-Oct-2023
https://doi.org/10.1109/ICCST59048.2023.10474230

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ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information Systems

May 2021

2053 pages

ISBN:9781450390200

DOI:10.1145/3469213

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 18 August 2021

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ICAIIS 2021: 2021 2nd International Conference on Artificial Intelligence and Information Systems

May 28 - 30, 2021

Chongqing, China

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Maniriho PMahmood AChowdhury M(2024)A systematic literature review on Windows malware detectionJournal of Systems and Software10.1016/j.jss.2023.111921209:COnline publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1016/j.jss.2023.111921
Srinu IVidyarthi D(2023)Classification of Malware Using Deep Learning: A Study2023 IEEE International Carnahan Conference on Security Technology (ICCST)10.1109/ICCST59048.2023.10474230(1-6)Online publication date: 11-Oct-2023
https://doi.org/10.1109/ICCST59048.2023.10474230

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A Survey on Malware Detection with Deep Learning

Machine/Deep Learning for obfuscated malware Detection

A new deep boosted CNN and ensemble learning based IoT malware detection

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