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A Network Attack Detection Method Using SDA and Deep Neural Network Based on Internet of Things

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Abstract

Aiming at the deficiency of network attack detection, a network attack detection method based on deep neural network is proposed. Firstly, the deep neural network technology is used to study the self-adaptive identification method of the security state, intelligently discriminate the security index of the network, recall comparative learning based on historical data, and establish the classification and identification database of network security. Then, according to the information of security classification and identification database, the corresponding state risk assessment system is mapped. Based on the risk intensity, different levels of early warnings are given. Finally, experimental simulation analysis is carried out to demonstrate the effectiveness of the proposed method. The simulation results show that the proposed method can actively send out early warning before the network is attacked, which obtains a high accuracy of early warning.

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Acknowledgement

This work was supported by the National Natural Science Foundation of China (No. 61802116) and the key scientific and technological project of Henan province (No. 192102210113).

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  1. College of Computer Science and Technology, Henan Institute of Technology, Xinxiang, 453002, Henan, China

    Jingwei Li & Bo Sun

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  1. Jingwei Li
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  2. Bo Sun
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Correspondence to Jingwei Li.

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Li, J., Sun, B. A Network Attack Detection Method Using SDA and Deep Neural Network Based on Internet of Things. Int J Wireless Inf Networks 27, 209–214 (2020). https://doi.org/10.1007/s10776-019-00462-7

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  • DOI: https://doi.org/10.1007/s10776-019-00462-7

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