Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (7): 2121-2126.doi: 10.13229/j.cnki.jdxbgxb.20220370

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Design of automatic identification algorithm for Internet of Things security situation based on deep neural network

Peng-ju LIU1,2()   

  1. 1.School of Cyber Science and Engineering，Wuhan University，Wuhan 430072，China
    2.Key Laboratory of Aerospace Information Security and Trusted Computing of Ministry of Education，Wuhan University，Wuhan 430072，China
  • Received:2022-04-21 Online:2023-07-01 Published:2023-07-20

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Abstract:

The unknown attack threat of the network will affect the security situation of the Internet of Things. In order to improve the identification effect， an automatic identification algorithm of the security situation of the Internet of Things based on deep neural network is proposed. Analyze the security threats of the Internet of Things through Trojan horse and DDoS attacks， obtain the security situation elements of the Internet of Things， extract the security situation characteristics of the Internet of Things by establishing the security state distribution model of the Internet of Things invaded by viruses， combine the deep neural network with the deep stack editor， train the extracted security situation characteristics， and use the final training results to determine the security situation performance of the Internet of Things， Complete the automatic identification of Internet of Things security situation and realize the design of automatic identification algorithm of Internet of Things security situation. The experimental results show that the algorithm has high accuracy and strong practicability through the comparison test of recognition effect， recall test， F1 value test and recognition running time test.

Key words: deep neural network, Internet of Things, security situation, automatic identification

CLC Number: 

  • TP393

Fig.1

Hierarchical IoT security situation element acquisition architecture"

Fig.2

Training process based on a deep neural network"

Fig.3

Identification accuracy and comparison results"

Table 1

Comparison test of recall rate of three methods"

物联网安全态势

数据集/个

不同方法的召回率/%
算法1算法2算法3
20098.993.293.6
40097.592.992.7
60097.491.691.5
80096.990.590.6
100096.589.389.7
120095.385.488.6

Fig.4

F1 value comparison test"

Fig.5

Identifying runtime tests"

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