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

Bichen Che1, Long Liu2,*, Huali Zhang1

1 School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing, China
2 College of New Media, Beijing Institute of Graphic Communication, Beijing, China

* Corresponding Author: Long Liu. Email:

Journal on Internet of Things 2020, 2(4), 145-162.


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.


Cite This Article

B. Che, L. Liu and H. Zhang, "Knemag: key node estimation mechanism based on attack graph for iot security," Journal on Internet of Things, vol. 2, no.4, pp. 145–162, 2020.


This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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