Abstract-Attack graphs play important roles in analyzing network security vulnerabilities, and previous works have provided meaningful conclusions on the generation and security measurement of attack graphs. However, it is still hard for us to understand attack graphs in a large network, and few suggestions have been proposed to prevent inside malicious attackers from attacking networks. To address these problems, we propose a novel approach to generate and describe attack graphs. Firstly, we construct a two-layer attack graph, where the upper layer is a hosts access graph and the lower layer is composed of some host-pair attack graphs. Compared with previous works, our attack graph has simpler structures, and reaches the best upper bound of computation cost in O(N 2 ). Furthermore, we introduce the adjacency matrix to efficiently evaluate network security, with overall evaluation results presented by gray scale images vividly. Thirdly, by applying prospective damage and important weight factors on key hosts with crucial resources, we can create prioritized lists of potential threatening hosts and stepping stones, both of which can help network administrators to harden network security. Analysis on computation cost shows that the upper bound computation cost of our measurement methodology is O(N 3 ), which could also be completed in real time. Finally, we give some examples to show how to put our methods in practice.
Since attack graphs provide practical attack context and relationships among vulnerabilities, researchers have been trying to evaluate network security based on attack graphs. However, previous works focus their attention on specific evaluations they concerned, and each does things in his own way. There is no explicit way telling network administrators how to measure network security in a general way. In this paper, we propose a new metric framework, whose main goal is to guide people to perform evaluations based on attack graphs. The main components of proposed metric framework include Security Index, Target of Evaluation, Elementary Attribute, Composition Algorithm, and Arithmetic operators. Relative definitions and analysis of these five components are also given. The following examples show the applications of our metric framework, and validate it.
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