Abstract
In large-scale networks, the state space is exploding and changing dynamically. This leads to difficulties in collecting and analyzing situational awareness data, so we construct an adaptive situational awareness model in spatio-temporal dimensions. In the spatial dimension, vulnerabilities’s threats are assessed through attack graphs combined with Shapley values. At the same time, vulnerability threats are dynamically quantified by updating the status node reachability probability in real time. In the temporal dimension, a game model is established by analyzing vulnerability attack graph nodes to dynamically adjust the observation frequency of high-risk vulnerabilities, focusing on the safety status characteristics of high-risk assets. Experimental results show that our proposal integrates the security features of both space and time dimensions. This method can better focus on high-risk vulnerabilities and accurately reflect the dynamic changes in the network security situation, ensuring timeliness and accuracy in network security detection.
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Acknowledgement
This research was supported by the National Natural Science Foundation of China under Grant No. 61672206, No. 61572170, Central Guide Local Science and Technology Development Fund Project (216Z0701G), S&T Program of Hebei under Grant No. 18210109D, No. 20310701D, No. 20310802D, No. 21310101D, National cultural and tourism science and technology innovation project (2020).
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Zhang, H., Xu, Y., Liu, B., Zhao, D., Bai, Y. (2024). Large-Scale Network Adaptive Situation Awareness Method in Spatio-Temporal Dimension. In: Jin, H., Pan, Y., Lu, J. (eds) Computer Networks and IoT. IAIC 2023. Communications in Computer and Information Science, vol 2060. Springer, Singapore. https://doi.org/10.1007/978-981-97-1332-5_6
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DOI: https://doi.org/10.1007/978-981-97-1332-5_6
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