基于随机森林算法的伪随机序列分类

A. Kozachok, A. Spirin, Alexander I. Kozachok, Alexey N. Tsibulia
{"title":"基于随机森林算法的伪随机序列分类","authors":"A. Kozachok, A. Spirin, Alexander I. Kozachok, Alexey N. Tsibulia","doi":"10.1109/IVMEM51402.2020.00016","DOIUrl":null,"url":null,"abstract":"Due to the increased number of information leaks caused by internal violators and the lack of mechanisms in modern DLP systems to counter information leaks in encrypted or compressed form, was proposed a method for classifying sequences formed by encryption and data compression algorithms. An algorithm for constructing a random forest was proposed, and the choice of classifier hyper parameters was justified. The presented approach showed the accuracy of classification of the sequences specified in the work 0.98.","PeriodicalId":325794,"journal":{"name":"2020 Ivannikov Memorial Workshop (IVMEM)","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2020-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"Classification of pseudo-random sequences based on the random forest algorithm\",\"authors\":\"A. Kozachok, A. Spirin, Alexander I. Kozachok, Alexey N. Tsibulia\",\"doi\":\"10.1109/IVMEM51402.2020.00016\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Due to the increased number of information leaks caused by internal violators and the lack of mechanisms in modern DLP systems to counter information leaks in encrypted or compressed form, was proposed a method for classifying sequences formed by encryption and data compression algorithms. An algorithm for constructing a random forest was proposed, and the choice of classifier hyper parameters was justified. The presented approach showed the accuracy of classification of the sequences specified in the work 0.98.\",\"PeriodicalId\":325794,\"journal\":{\"name\":\"2020 Ivannikov Memorial Workshop (IVMEM)\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-09-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 Ivannikov Memorial Workshop (IVMEM)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/IVMEM51402.2020.00016\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 Ivannikov Memorial Workshop (IVMEM)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IVMEM51402.2020.00016","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

摘要

针对内部违规者导致的信息泄露事件增多,以及现代DLP系统中缺乏对抗加密或压缩形式信息泄露的机制,提出了一种对加密和数据压缩算法形成的序列进行分类的方法。提出了一种构造随机森林的算法,并对分类器超参数的选择进行了论证。该方法对工作中指定序列的分类准确率为0.98。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Classification of pseudo-random sequences based on the random forest algorithm
Due to the increased number of information leaks caused by internal violators and the lack of mechanisms in modern DLP systems to counter information leaks in encrypted or compressed form, was proposed a method for classifying sequences formed by encryption and data compression algorithms. An algorithm for constructing a random forest was proposed, and the choice of classifier hyper parameters was justified. The presented approach showed the accuracy of classification of the sequences specified in the work 0.98.
求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
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