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Defensive deception framework against reconnaissance attacks in the cloud with deep reinforcement learning

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Abstract

Implementing defensive deception in the cloud is promising to proactively counter reconnaissance attack. This technique presents decoys to camouflage cloud assets and distracts attack resource. However, the major challenge is to develop an effective deception strategy to orchestrate digital decoys. To address this issue, we propose a deep reinforcement learning (DRL)-based defensive deception framework. First, we formulate a utility function, which mathematically models underlying threats associated with common vulnerabilities among virtual machines in the cloud. Then, we customize training interfaces and the neural networks for a DRL agent. The reward function reflects the effectiveness of asset concealment and the waste of attack resources, referring to a comprehensive defense goal. Finally, the well-trained DRL agent generates the optimal defense strategy. It specifies a more granular deception strategy than existing proposals. Simulation results show that the proposed framework leads to a 7.87% average advantage in realizing the comprehensive defense goal. Moreover, it can stably improve the concealment degree of cloud assets up to 20.58%, and increase the attack cost up to 40.40%. This study shows that it is promising to improve cloud security with deception defense and artificial intelligence techniques.

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Acknowledgements

This work has been partly supported by National Key Research and Development Program of China (Grant Nos. 2021YFB1006200, 2021YFB1006201) and National Natural Science Foundation of China (Grant Nos. 62072467, 62002383).

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Authors and Affiliations

  1. Department of Computer Science, Information Engineering University, Zhengzhou, 450001, China

    Huanruo Li, Yunfei Guo, Shumin Huo, Hongchao Hu & Penghao Sun

  2. Department of Communication Technologies, Academy of Military Science, Beijing, 100091, China

    Penghao Sun

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  1. Huanruo Li
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  2. Yunfei Guo
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  3. Shumin Huo
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  4. Hongchao Hu
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  5. Penghao Sun
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Correspondence to Huanruo Li.

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Li, H., Guo, Y., Huo, S. et al. Defensive deception framework against reconnaissance attacks in the cloud with deep reinforcement learning. Sci. China Inf. Sci. 65, 170305 (2022). https://doi.org/10.1007/s11432-021-3462-4

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