Research on active learning based computer viruses detection approaches

Ou Qingyu, Z. Dawei
{"title":"Research on active learning based computer viruses detection approaches","authors":"Ou Qingyu, Z. Dawei","doi":"10.1109/CINC.2010.5643779","DOIUrl":null,"url":null,"abstract":"As traditional computer viruses detection approaches update slowly and have poor ability in detecting unknown viruses, active learning is well-suited to many problems in viruses detect processing, where unlabeled data may be abundant but annotationis slow and expensive. This paper aim to shed light on the application of the active learning theory in computer viruses detection. Moreover, to improve the precision of the virus detection and the efficiency of the active learning process, query function based on the uncertainty based sampling is realized. Experiments' results show that the model has very good detection precision against unknown computer viruses and can greatly shorten the training time and reduce the requirements of the training data and improve the learning efficiency of the system.","PeriodicalId":227004,"journal":{"name":"2010 Second International Conference on Computational Intelligence and Natural Computing","volume":"46 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2010-11-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2010 Second International Conference on Computational Intelligence and Natural Computing","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CINC.2010.5643779","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

As traditional computer viruses detection approaches update slowly and have poor ability in detecting unknown viruses, active learning is well-suited to many problems in viruses detect processing, where unlabeled data may be abundant but annotationis slow and expensive. This paper aim to shed light on the application of the active learning theory in computer viruses detection. Moreover, to improve the precision of the virus detection and the efficiency of the active learning process, query function based on the uncertainty based sampling is realized. Experiments' results show that the model has very good detection precision against unknown computer viruses and can greatly shorten the training time and reduce the requirements of the training data and improve the learning efficiency of the system.
基于主动学习的计算机病毒检测方法研究
由于传统的计算机病毒检测方法更新缓慢,对未知病毒的检测能力较差,主动学习非常适合解决病毒检测处理中的许多问题,这些问题中未标记数据可能很丰富,但注释速度慢且成本高。本文旨在阐明主动学习理论在计算机病毒检测中的应用。此外,为了提高病毒检测的精度和主动学习过程的效率,实现了基于不确定性采样的查询功能。实验结果表明,该模型对未知计算机病毒具有很好的检测精度,可以大大缩短训练时间,降低对训练数据的要求,提高系统的学习效率。
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:481959085
Book学术官方微信