Malware Classification Method Using API Call Categorization

Andre Wijaya, Charles Lim, Yohanes Syailendra Kotualubun
{"title":"Malware Classification Method Using API Call Categorization","authors":"Andre Wijaya, Charles Lim, Yohanes Syailendra Kotualubun","doi":"10.1145/3557738.3557851","DOIUrl":null,"url":null,"abstract":"The development of malware and computer security countermeasures is in a continuous arms race. Malware authors will adapt their malware according to the current state of events to maximize their chance of success. This increases the value of rapidly detecting the presence of malware within a system and identifying the type of malware. This research proposes a new method of classifying malware using API call categorization based on markov chain. The proposed methods have demonstrated a moderate accuracy of 87.19% with an f-1 score of 75.18%.","PeriodicalId":178760,"journal":{"name":"Proceedings of the 2022 International Conference on Engineering and Information Technology for Sustainable Industry","volume":"91 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2022 International Conference on Engineering and Information Technology for Sustainable Industry","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3557738.3557851","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0

Abstract

The development of malware and computer security countermeasures is in a continuous arms race. Malware authors will adapt their malware according to the current state of events to maximize their chance of success. This increases the value of rapidly detecting the presence of malware within a system and identifying the type of malware. This research proposes a new method of classifying malware using API call categorization based on markov chain. The proposed methods have demonstrated a moderate accuracy of 87.19% with an f-1 score of 75.18%.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
基于API调用分类的恶意软件分类方法
恶意软件和计算机安全对策的发展处于持续的军备竞赛中。恶意软件的作者将根据事件的当前状态调整他们的恶意软件,以最大限度地提高他们成功的机会。这增加了快速检测系统中存在的恶意软件和识别恶意软件类型的价值。本文提出了一种基于马尔可夫链的API调用分类的恶意软件分类方法。该方法的准确率为87.19%,f-1评分为75.18%。
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 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Color Palettes Overview After Thresholding Process with Default Methods of ImageJ or FIJI∗ Application of Six Sigma in Quality Improvement of Deodorant Products at PT Cedefindo Controlling The Performance of Anti-lock Braking System at Various Tracks and Vehicle Conditions Assessment Risk Ergonomic in Painting Industry using Ergo-FMEA: Assessment Risk Ergonomic in Painting Industry using Ergo-FMEA A Combined Application of SERVQUAL and fuzzy DEMATEL to Evaluate a University's Service Quality
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1