贝叶斯攻击图上部分可观察扩散过程的最优防御策略

Erik Miehling, M. Rasouli, D. Teneketzis
{"title":"贝叶斯攻击图上部分可观察扩散过程的最优防御策略","authors":"Erik Miehling, M. Rasouli, D. Teneketzis","doi":"10.1145/2808475.2808482","DOIUrl":null,"url":null,"abstract":"The defense of computer networks from intruders is becoming a problem of great importance as networks and devices become increasingly connected. We develop an automated approach to defending a network against continuous attacks from intruders, using the notion of Bayesian attack graphs to describe how attackers combine and exploit system vulnerabilities in order to gain access and progress through a network. We assume that the attacker follows a probabilistic spreading process on the attack graph and that the defender can only partially observe the attacker's capabilities at any given time. This leads to the formulation of the defender's problem as a partially observable Markov decision process (POMDP). We define and compute optimal defender countermeasure policies, which describe the optimal countermeasure action to deploy given the current information.","PeriodicalId":20578,"journal":{"name":"Proceedings of the Second ACM Workshop on Moving Target Defense","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2015-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"75","resultStr":"{\"title\":\"Optimal Defense Policies for Partially Observable Spreading Processes on Bayesian Attack Graphs\",\"authors\":\"Erik Miehling, M. Rasouli, D. Teneketzis\",\"doi\":\"10.1145/2808475.2808482\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The defense of computer networks from intruders is becoming a problem of great importance as networks and devices become increasingly connected. We develop an automated approach to defending a network against continuous attacks from intruders, using the notion of Bayesian attack graphs to describe how attackers combine and exploit system vulnerabilities in order to gain access and progress through a network. We assume that the attacker follows a probabilistic spreading process on the attack graph and that the defender can only partially observe the attacker's capabilities at any given time. This leads to the formulation of the defender's problem as a partially observable Markov decision process (POMDP). We define and compute optimal defender countermeasure policies, which describe the optimal countermeasure action to deploy given the current information.\",\"PeriodicalId\":20578,\"journal\":{\"name\":\"Proceedings of the Second ACM Workshop on Moving Target Defense\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2015-10-12\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"75\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the Second ACM Workshop on Moving Target Defense\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2808475.2808482\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the Second ACM Workshop on Moving Target Defense","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2808475.2808482","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 75

摘要

随着网络和设备的连接日益紧密,计算机网络的防御已成为一个非常重要的问题。我们开发了一种自动化的方法来保护网络免受入侵者的持续攻击,使用贝叶斯攻击图的概念来描述攻击者如何结合和利用系统漏洞,以便通过网络获得访问和进展。我们假设攻击者在攻击图上遵循概率扩散过程,并且防御者在任何给定时间只能部分地观察到攻击者的能力。这导致将防守者的问题表述为部分可观察的马尔可夫决策过程(POMDP)。我们定义并计算了最优防御对策策略,该策略描述了在给定当前信息的情况下部署的最优对策行动。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Optimal Defense Policies for Partially Observable Spreading Processes on Bayesian Attack Graphs
The defense of computer networks from intruders is becoming a problem of great importance as networks and devices become increasingly connected. We develop an automated approach to defending a network against continuous attacks from intruders, using the notion of Bayesian attack graphs to describe how attackers combine and exploit system vulnerabilities in order to gain access and progress through a network. We assume that the attacker follows a probabilistic spreading process on the attack graph and that the defender can only partially observe the attacker's capabilities at any given time. This leads to the formulation of the defender's problem as a partially observable Markov decision process (POMDP). We define and compute optimal defender countermeasure policies, which describe the optimal countermeasure action to deploy given the current information.
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