Application of IoT technology in cyber security prevention system

被引：0
作者：
Dong, Jiahan [1 ]
Wang, Chao [1 ]
Guo, Guangxin [1 ]
Ren, Tianyu [1 ]
Sun, Hao [2 ]
机构：
[1] State Grid Beijing Electric Power Research, Beijing
[2] AnHui JiYuan Inspection and Testing Technology Co., LTD, Anhui, Hefei
来源：
Applied Mathematics and Nonlinear Sciences | 2024年 / 9卷 / 01期
关键词：
Effective signal-to-noise ratio; Elliptic curve encryption algorithm; Internet of Things technology; MEMM; Network security prevention system; UCB algorithm;
D O I：
10.2478/amns-2024-2266
中图分类号：
学科分类号：
摘要：
In the process of gradually expanding the scale of computer networks and the design of network systems becoming more and more complex, people pay more and more attention to the construction of network security protection systems. Starting from the blockchain encryption technology, the article establishes the authentication and access management key based on the elliptic curve encryption algorithm and combines the maximum entropy model with the hidden Markov model to construct the MEMM for intrusion detection of network security. Based on the effective signal-to-noise ratio model of the network channel, an adaptive channel selection strategy based on the UCB algorithm is proposed. The IoT security prevention system is built based on IoT technology, and each functional module of the system is designed. The system’s authentication security, network intrusion detection, adaptive channel selection, and concurrency performance were tested after the design was completed. The encryption operation time of the ECC algorithm was improved by 41.53% compared to the RSA algorithm, the average time of the MEMM network intrusion detection was 41.54ms, and the false alarm rate of the intrusion detection was kept below 16.5%. The average packet collection rate of the nodes in the adaptive channel selection algorithm is 90.98%. The maximum system throughput is up to 62.19MB, and the extreme difference in data volume between different nodes is only 38 entries. Constructing a network security prevention system based on IoT technology and combining multiple encryption techniques can ensure the secure transmission of network data. © 2024 Jiahan Dong, Chao Wang, Guangxin Guo, Tianyu Ren and Hao Sun, published by Sciendo.
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  • [1] Jeeshitha J., Ramakoteswararao G., Extensive study on ddosbotnet attacks in multiple environments using deep learning and machine learning techniques, ECS transactions, 1, (2022)
  • [2] Dao N.N., Phan T.V., Ad U.S., Kim J., Bauschert T., Cho S., Securing heterogeneous iot with intelligent ddos attack behavior learning, IEEE Communications Magazine, (2017)
  • [3] Febro A., Xiao H., Spring J., Christianson B., Edge security for sip-enabled iot devices with p4, Computer networks, 11, (2022)
  • [4] Gao J., A support vector machine model for computer network security technology, Boletin Tecnico/Technical Bulletin, 55, 12, pp. 564-568, (2017)
  • [5] Aoudni Y., Donald C., Farouk A., Sahay K.B., Babu D.V., Tripathi V., Et al., Cloud security based attack detection using transductive learning integrated with hidden markov model, Pattern recognition letters, (2022)
  • [6] Midi D., Sultana S., Bertino, Elisa, A system for response and prevention of security incidents in wireless sensor networks, ACM transactions on sensor networks, 13, 1, (2017)
  • [7] Liu T., Liu Y., Liu J., Wang L., Xu L., Qiu G., Et al., A bayesian learning based scheme for online dynamic security assessment and preventive control, IEEE Transactions on Power Systems, 35, 5, pp. 4088-4099, (2020)
  • [8] Bulrajoul W., James A., Shaikh S., A new architecture for network intrusion detection and prevention, IEEE Access, (2019)
  • [9] Zardari Z.A., He J., Pathan M.S., Zhu N., Detection and prevention of jellyfish attacks using knn algorithm and trusted routing scheme in manet, International Journal of Network Security, (2021)
  • [10] Farhaoui Y., Design and implementation of an intrusion prevention system, International Journal of Network Security, 19, (2017)
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