Malware Detection in PE files using Machine Learning

Samarth Tyagi, Achintya Baghela, Kashif Majid Dar, Anwesh Patel, Sonali Kothari, Snehal Bhosale
{"title":"Malware Detection in PE files using Machine Learning","authors":"Samarth Tyagi, Achintya Baghela, Kashif Majid Dar, Anwesh Patel, Sonali Kothari, Snehal Bhosale","doi":"10.1109/OTCON56053.2023.10113998","DOIUrl":null,"url":null,"abstract":"Malware has become one of the most challenging threats to the computer domain. Malware is malicious code mainly used to gain access and collect confidential information without permission. The internet coverage has boomed a lot in today’s time leading to people downloading various files and installing executable files like.exe,.bat, and.msi files. This leads to many complications as these files are the vector for malicious code. Through this paper, we present a technique to detect executable files as malicious by a detailed search of the Portable Executable (PE) files that come along with the executable files. Our approach uses the static analysis technique to get features from PE files. We use these with supervised learning algorithms to classify malware. We also compare the performance of different algorithms to determine the best way to approach our problem.","PeriodicalId":265966,"journal":{"name":"2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development (OTCON)","volume":"218 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-02-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 OPJU International Technology Conference on Emerging Technologies for Sustainable Development (OTCON)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/OTCON56053.2023.10113998","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

Malware has become one of the most challenging threats to the computer domain. Malware is malicious code mainly used to gain access and collect confidential information without permission. The internet coverage has boomed a lot in today’s time leading to people downloading various files and installing executable files like.exe,.bat, and.msi files. This leads to many complications as these files are the vector for malicious code. Through this paper, we present a technique to detect executable files as malicious by a detailed search of the Portable Executable (PE) files that come along with the executable files. Our approach uses the static analysis technique to get features from PE files. We use these with supervised learning algorithms to classify malware. We also compare the performance of different algorithms to determine the best way to approach our problem.
使用机器学习的PE文件中的恶意软件检测
恶意软件已成为计算机领域最具挑战性的威胁之一。恶意软件是主要用于未经许可获取和收集机密信息的恶意代码。互联网覆盖范围在今天已经蓬勃发展,导致人们下载各种文件并安装可执行文件,如。exe,。bat和。msi文件。这导致了许多复杂性,因为这些文件是恶意代码的载体。通过本文,我们提出了一种通过详细搜索可执行文件附带的可移植可执行文件(PE)来检测可执行文件是否为恶意文件的技术。我们的方法使用静态分析技术从PE文件中获取特征。我们使用这些与监督学习算法来分类恶意软件。我们还比较了不同算法的性能,以确定解决问题的最佳方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
自引率
0.00%
发文量
0
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:604180095
Book学术官方微信