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Adaboost-based security level classification of mobile intelligent terminals

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Abstract

With the rapid development of Internet of Things, massive mobile intelligent terminals are ready to access edge servers for real-time data calculation and interaction. However, the risk of private data leakage follows simultaneously. As the administrator of all intelligent terminals in a region, the edge server needs to clarify the ability of the managed intelligent terminals to defend against malicious attacks. Therefore, the security level classification for mobile intelligent terminals before accessing the network is indispensable. In this paper, we firstly propose a safety assessment method to detect the weakness of mobile intelligent terminals. Secondly, we match the evaluation results to the security level. Finally, a scheme of security level classification for mobile intelligent terminals based on Adaboost algorithm is proposed. The experimental results demonstrate that compared to a baseline that statistically calculates the security level, the proposed method can complete the security level classification with lower latency and high accuracy when massive mobile intelligent terminals access the network at the same time.

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Acknowledgements

This work was supported by National Natural Science Foundation of China (No. 61571104), Sichuan Science and Technology Program (No. 2018JY0539), Key projects of the Sichuan Provincial Education Department (No. 18ZA0219), Fundamental Research Funds for the Central Universities (No. ZYGX2017KYQD170) and Innovation Funding (No. 2018510007000134). The authors wish to thank the reviewers for their helpful comments. Dr. Dingde Jiang and Dr. Hong Wen are corresponding authors of this paper.

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Authors and Affiliations

  1. School of Astronautics and Aeronautic, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Feng Wang, Dingde Jiang & Hong Wen

  2. Department of Electrical Computer, Software, and Systems Engineering, Embry-Riddle Aeronautical University, Daytona Beach, FL, 32114, USA

    Houbing Song

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  1. Feng Wang
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  2. Dingde Jiang
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  3. Hong Wen
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  4. Houbing Song
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Correspondence to Houbing Song.

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Wang, F., Jiang, D., Wen, H. et al. Adaboost-based security level classification of mobile intelligent terminals. J Supercomput 75, 7460–7478 (2019). https://doi.org/10.1007/s11227-019-02954-y

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