A Moving Target Defense Approach to Disrupting Stealthy Botnets

S. Venkatesan, Massimiliano Albanese, G. Cybenko, S. Jajodia
{"title":"A Moving Target Defense Approach to Disrupting Stealthy Botnets","authors":"S. Venkatesan, Massimiliano Albanese, G. Cybenko, S. Jajodia","doi":"10.1145/2995272.2995280","DOIUrl":null,"url":null,"abstract":"Botnets are increasingly being used for exfiltrating sensitive data from mission-critical systems. Research has shown that botnets have become extremely sophisticated and can operate in stealth mode by minimizing their host and network footprint. In order to defeat exfiltration by modern botnets, we propose a moving target defense approach for dynamically deploying detectors across a network. Specifically, we propose several strategies based on centrality measures to periodically change the placement of detectors. Our objective is to increase the attacker's effort and likelihood of detection by creating uncertainty about the location of detectors and forcing botmasters to perform additional actions in an attempt to create detector-free paths through the network. We present metrics to evaluate the proposed strategies and an algorithm to compute a lower bound on the detection probability. We validate our approach through simulations, and results confirm that the proposed solution effectively reduces the likelihood of successful exfiltration campaigns.","PeriodicalId":20539,"journal":{"name":"Proceedings of the 2016 ACM Workshop on Moving Target Defense","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2016-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"38","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2016 ACM Workshop on Moving Target Defense","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2995272.2995280","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 38

Abstract

Botnets are increasingly being used for exfiltrating sensitive data from mission-critical systems. Research has shown that botnets have become extremely sophisticated and can operate in stealth mode by minimizing their host and network footprint. In order to defeat exfiltration by modern botnets, we propose a moving target defense approach for dynamically deploying detectors across a network. Specifically, we propose several strategies based on centrality measures to periodically change the placement of detectors. Our objective is to increase the attacker's effort and likelihood of detection by creating uncertainty about the location of detectors and forcing botmasters to perform additional actions in an attempt to create detector-free paths through the network. We present metrics to evaluate the proposed strategies and an algorithm to compute a lower bound on the detection probability. We validate our approach through simulations, and results confirm that the proposed solution effectively reduces the likelihood of successful exfiltration campaigns.
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
破坏隐形僵尸网络的移动目标防御方法
僵尸网络越来越多地被用于从关键任务系统中窃取敏感数据。研究表明,僵尸网络已经变得非常复杂,可以通过最小化其主机和网络足迹来以隐身模式运行。为了挫败现代僵尸网络的渗透,我们提出了一种动态部署检测器的移动目标防御方法。具体来说,我们提出了几种基于中心性度量的策略来周期性地改变检测器的位置。我们的目标是增加攻击者的努力和检测的可能性,通过创建检测器位置的不确定性,并迫使管理员执行额外的操作,试图在网络中创建无检测器的路径。我们提出了评估所提出的策略的指标和计算检测概率下界的算法。我们通过模拟验证了我们的方法,结果证实了所提出的解决方案有效地降低了成功渗透活动的可能性。
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
Moving Target Defense: a Journey from Idea to Product Session details: Keynote Talk Automated Effectiveness Evaluation of Moving Target Defenses: Metrics for Missions and Attacks Markov Modeling of Moving Target Defense Games Session details: Invited Industry Talk
×
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