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摘要:
Network attack graphs are originally used to evaluate what the worst security state is when a concerned net-work is under attack. Combined with intrusion evidence such like IDS alerts, attack graphs can be further used to perform security state posterior inference (i.e. inference based on observation experience). In this area, Bayesian network is an ideal mathematic tool, however it can not be directly applied for the following three reasons: 1) in a network attack graph, there may exist directed cycles which are never permitted in a Bayesian network, 2) there may exist temporal partial ordering relations among intrusion evidence that can-not be easily modeled in a Bayesian network, and 3) just one Bayesian network cannot be used to infer both the current and the future security state of a network. In this work, we improve an approximate Bayesian posterior inference algorithm–the likelihood-weighting algorithm to resolve the above obstacles. We give out all the pseudocodes of the algorithm and use several examples to demonstrate its benefit. Based on this, we further propose a network security assessment and enhancement method along with a small network scenario to exemplify its usage.
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篇名 A Novel Attack Graph Posterior Inference Model Based on Bayesian Network
来源期刊 信息安全(英文) 学科 工学
关键词 NETWORK Security ATTACK Graph POSTERIOR INFERENCE Bayesian NETWORK Likelihood-Weighting
年,卷(期) 2011,(1) 所属期刊栏目
研究方向 页码范围 8-27
页数 20页 分类号 TP39
字数 语种
DOI
五维指标
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研究主题发展历程
节点文献
NETWORK
Security
ATTACK
Graph
POSTERIOR
INFERENCE
Bayesian
NETWORK
Likelihood-Weighting
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研究去脉
引文网络交叉学科
相关学者/机构
期刊影响力
信息安全(英文)
季刊
2153-1234
武汉市江夏区汤逊湖北路38号光谷总部空间
出版文献量(篇)
230
总下载数(次)
0
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