Abstract
Traffic flows can be forwarded through different security service functions based on SDN/NFV technology, which constitutes security service function chaining (SFC). However, the current deployed security service function chaining cannot be dynamically adjusted according to the state of the network environment, and cannot adapt to the rapidly changing security requirements. This paper proposes a security SFC path selection scheme based on deep reinforcement learning. The optimal path of security SFC is dynamically selected in real time using the DQN algorithm, according to the features of the traffic entering the SFC and the detection results of the security service functions. The security capability of the SFC is improved and the latency of the SFC is reduced under the optimal path. We design and implemented a prototype system of this scheme, conduct experiments with DDoS detection security function, and compare the proposed DQN algorithm with Q-learning algorithm. The results show that SFC path selection by DQN algorithm can effectively improve the average DDoS attack detection rate and reduce the latency.
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Acknowledgments
This paper is supported by National Key R &D Program of China under Grant No. 2018YFA0701604.
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Deng, S., Li, M., Guo, Q., Zhou, H. (2023). Security SFC Path Selection Using Deep Reinforcement Learning. In: You, I., Kim, H., Angin, P. (eds) Mobile Internet Security. MobiSec 2022. Communications in Computer and Information Science, vol 1644. Springer, Singapore. https://doi.org/10.1007/978-981-99-4430-9_7
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DOI: https://doi.org/10.1007/978-981-99-4430-9_7
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