KNEMAG: Key Node Estimation Mechanism Based on Attack Graph for IoT Security

Bichen Che, Longguo Liu, Huali Zhang
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引用次数: 7

Abstract

: With the rapid development and widespread application of the IoT, the at-tacks against IoT vulnerabilities have become more complex and diverse. Most of the previous research focused on node vulnerability and its risk analysis. There is little information available about the importance of the location of the node in the system. Therefore, an estimation mechanism is proposed to assess the key node of the IoT system. The estimation of the key node includes two parts: one is the utilization relationship between nodes, and the other is the impact on the system after the node is conquered. We use the node importance value and the node risk value to quantify these two parts. First, the node importance value is calculated by considering the attack path that pass through the node and the probability that the attacker will abandon the attack. Second, in addition to node vulnerabilities and the consequences of being attacked, two quantitative indicators are proposed to comprehensively assess the impact of nodes on the system security, and the node risk value is calculated based on the grey correlation analysis method. Third, the key node in the IoT system could be obtained by integrating the node importance value and risk value. Finally, the simulation experiment result shows that the presented method could find the key node of the system quickly and accurately.
基于攻击图的物联网安全关键节点估计机制
随着物联网的快速发展和广泛应用,针对物联网漏洞的攻击变得更加复杂和多样。以往的研究大多集中在节点漏洞及其风险分析上。关于节点在系统中位置的重要性的信息很少。为此,提出了一种评估机制,对物联网系统的关键节点进行评估。关键节点的估计包括两个部分:一是节点之间的利用关系,二是节点被征服后对系统的影响。我们用节点重要性值和节点风险值来量化这两部分。首先,考虑经过节点的攻击路径和攻击者放弃攻击的概率,计算节点重要性值。其次,除了节点脆弱性和被攻击后果外,提出了两个量化指标来综合评估节点对系统安全的影响,并基于灰色关联分析法计算节点风险值。第三,通过对节点重要性值和风险值的综合,得到物联网系统中的关键节点。最后,仿真实验结果表明,该方法能够快速准确地找到系统的关键节点。
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