DomainProfiler: Discovering Domain Names Abused in Future

Daiki Chiba, Takeshi Yagi, Mitsuaki Akiyama, Toshiki Shibahara, T. Yada, Tatsuya Mori, Shigeki Goto
{"title":"DomainProfiler: Discovering Domain Names Abused in Future","authors":"Daiki Chiba, Takeshi Yagi, Mitsuaki Akiyama, Toshiki Shibahara, T. Yada, Tatsuya Mori, Shigeki Goto","doi":"10.1109/DSN.2016.51","DOIUrl":null,"url":null,"abstract":"Cyber attackers abuse the domain name system (DNS) to mystify their attack ecosystems, they systematically generate a huge volume of distinct domain names to make it infeasible for blacklisting approaches to keep up with newly generated malicious domain names. As a solution to this problem, we propose a system for discovering malicious domain names that will likely be abused in future. The key idea with our system is to exploit temporal variation patterns (TVPs) of domain names. The TVPs of domain names include information about how and when a domain name has been listed in legitimate/popular and/or malicious domain name lists. On the basis of this idea, our system actively collects DNS logs, analyzes their TVPs, and predicts whether a given domain name will be used for malicious purposes. Our evaluation revealed that our system can predict malicious domain names 220 days beforehand with a true positive rate of 0.985.","PeriodicalId":102292,"journal":{"name":"2016 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-09-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"32","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 46th Annual IEEE/IFIP International Conference on Dependable Systems and Networks (DSN)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/DSN.2016.51","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 32

Abstract

Cyber attackers abuse the domain name system (DNS) to mystify their attack ecosystems, they systematically generate a huge volume of distinct domain names to make it infeasible for blacklisting approaches to keep up with newly generated malicious domain names. As a solution to this problem, we propose a system for discovering malicious domain names that will likely be abused in future. The key idea with our system is to exploit temporal variation patterns (TVPs) of domain names. The TVPs of domain names include information about how and when a domain name has been listed in legitimate/popular and/or malicious domain name lists. On the basis of this idea, our system actively collects DNS logs, analyzes their TVPs, and predicts whether a given domain name will be used for malicious purposes. Our evaluation revealed that our system can predict malicious domain names 220 days beforehand with a true positive rate of 0.985.
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
DomainProfiler:发现未来滥用的域名
网络攻击者滥用域名系统(DNS)来神秘化其攻击生态系统,他们系统地生成大量不同的域名,使黑名单方法无法跟上新生成的恶意域名。为了解决这个问题,我们提出了一个系统来发现将来可能被滥用的恶意域名。该系统的关键思想是利用域名的时间变化模式(TVPs)。域名的tvp包括有关域名如何及何时被列入合法/流行及/或恶意域名名单的资料。基于这个想法,我们的系统主动收集DNS日志,分析它们的tvp,并预测给定的域名是否会被用于恶意目的。我们的评估表明,我们的系统可以提前220天预测恶意域名,真阳性率为0.985。
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
ELZAR: Triple Modular Redundancy Using Intel AVX (Practical Experience Report) DomainProfiler: Discovering Domain Names Abused in Future OSIRIS: Efficient and Consistent Recovery of Compartmentalized Operating Systems HSFI: Accurate Fault Injection Scalable to Large Code Bases Secure and Efficient Multi-Variant Execution Using Hardware-Assisted Process Virtualization
×
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