Abstract
The traditional information security protection cannot prevent the malicious and directed intrusion of the network. To discover potential risks comprehensively, accurately, and timely, the polymorphic heterogeneous executor is constructed to confuse the attacker, called mimicry multimode decision. However, heterogeneous executors are composed of complex hardware, systems and applications, so how to select the optimal combination to face the potential risks becomes a problem. This paper proposes a mimicry multimode decision scheme based on Adaboost machine learning algorithm. The administrator can utilize Adaboost classifier to adaptively select the combination of the most defensible executor, so as to realize mimicry multimode defense and improve the security of applications. Simulation results demonstrate that the adaptive mimicry multimode decision method is promising.
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Acknowledgements
This work was supported in part by the National Natural Science Foundation of China (No. 61571104), the Sichuan Science and Technology Program (No. 2018JY0539), the Key projects of the Sichuan Provincial Education Department (No. 18ZA0219), the Fundamental Research Funds for the Central Universities (No. ZYGX2017KYQD170), the CERNET Innovation Project (No. NGII20190111), the Fund Project (Nos. 61403110405, 315075802), and the Innovation Funding (No. 2018510007000134). The authors wish to thank the reviewers for their helpful comments.
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Wang, F., Jiang, D., Wang, Z., Chen, Y. (2021). An Adaptive Algorithm Based on Adaboost for Mimicry Multimode Decisions. In: Song, H., Jiang, D. (eds) Simulation Tools and Techniques. SIMUtools 2020. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 369. Springer, Cham. https://doi.org/10.1007/978-3-030-72792-5_12
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DOI: https://doi.org/10.1007/978-3-030-72792-5_12
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