Design of Intrusion Detection System for Internet of Things Based on Improved BP Neural Network
With the advent of global 5G networks, the Internet of Things will no longer be limited by network speed and traffic. With the large-scale application of the Internet of Things, people pay more and more attention to the security of the Internet of Things. Once the Internet of Things system suffers f...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8766804/ |
| _version_ | 1818949171980271616 |
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| author | Aimin Yang Yunxi Zhuansun Chenshuai Liu Jie Li Chunying Zhang |
| author_facet | Aimin Yang Yunxi Zhuansun Chenshuai Liu Jie Li Chunying Zhang |
| author_sort | Aimin Yang |
| collection | DOAJ |
| description | With the advent of global 5G networks, the Internet of Things will no longer be limited by network speed and traffic. With the large-scale application of the Internet of Things, people pay more and more attention to the security of the Internet of Things. Once the Internet of Things system suffers from malicious attacks, not only the serious loss of information will lead to the paralysis of the Internet of Things equipment. Aiming at the security problem of the Internet of Things, this paper puts forward the LM-BP neural network model. The LM-BP neural network model is applied to an intrusion detection system, and the intrusion detection flow under LM-BP algorithm is given. LM algorithm has the characteristics of fast optimization speed and strong robustness and uses this characteristic to optimize the weight threshold of traditional BP neural network. Through establishing LM-BP neural network classifier, KDD CUP 99 intrusion detection data set is imported into an LM-BP neural network classifier, and the best results are obtained through continuous training. Finally, the experimental simulation results show that this model has higher detection rate and lower false alarm rate than the traditional BP neural network model and PSO-BP neural network model for DOS, R2L, U2L, and Probing, thus this modified model has certain promotion value. |
| first_indexed | 2024-12-20T08:58:28Z |
| format | Article |
| id | doaj.art-c391007368ed471ab704bf6844adaeb8 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T08:58:28Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-c391007368ed471ab704bf6844adaeb82022-12-21T19:45:56ZengIEEEIEEE Access2169-35362019-01-01710604310605210.1109/ACCESS.2019.29299198766804Design of Intrusion Detection System for Internet of Things Based on Improved BP Neural NetworkAimin Yang0https://orcid.org/0000-0003-0463-9023Yunxi Zhuansun1Chenshuai Liu2Jie Li3https://orcid.org/0000-0002-9198-5321Chunying Zhang4College of Science, North China University of Science and Technology, Tangshan, ChinaCollege of Science, North China University of Science and Technology, Tangshan, ChinaThe Key Laboratory of Engineering Calculation in Tangshan City, North China University of Science and Technology, Tangshan, ChinaThe Key Laboratory of Engineering Calculation in Tangshan City, North China University of Science and Technology, Tangshan, ChinaCollege of Science, North China University of Science and Technology, Tangshan, ChinaWith the advent of global 5G networks, the Internet of Things will no longer be limited by network speed and traffic. With the large-scale application of the Internet of Things, people pay more and more attention to the security of the Internet of Things. Once the Internet of Things system suffers from malicious attacks, not only the serious loss of information will lead to the paralysis of the Internet of Things equipment. Aiming at the security problem of the Internet of Things, this paper puts forward the LM-BP neural network model. The LM-BP neural network model is applied to an intrusion detection system, and the intrusion detection flow under LM-BP algorithm is given. LM algorithm has the characteristics of fast optimization speed and strong robustness and uses this characteristic to optimize the weight threshold of traditional BP neural network. Through establishing LM-BP neural network classifier, KDD CUP 99 intrusion detection data set is imported into an LM-BP neural network classifier, and the best results are obtained through continuous training. Finally, the experimental simulation results show that this model has higher detection rate and lower false alarm rate than the traditional BP neural network model and PSO-BP neural network model for DOS, R2L, U2L, and Probing, thus this modified model has certain promotion value.https://ieeexplore.ieee.org/document/8766804/Intrusion detection systemKDD CUP 99 datasetLM-BP neural network model |
| spellingShingle | Aimin Yang Yunxi Zhuansun Chenshuai Liu Jie Li Chunying Zhang Design of Intrusion Detection System for Internet of Things Based on Improved BP Neural Network IEEE Access Intrusion detection system KDD CUP 99 dataset LM-BP neural network model |
| title | Design of Intrusion Detection System for Internet of Things Based on Improved BP Neural Network |
| title_full | Design of Intrusion Detection System for Internet of Things Based on Improved BP Neural Network |
| title_fullStr | Design of Intrusion Detection System for Internet of Things Based on Improved BP Neural Network |
| title_full_unstemmed | Design of Intrusion Detection System for Internet of Things Based on Improved BP Neural Network |
| title_short | Design of Intrusion Detection System for Internet of Things Based on Improved BP Neural Network |
| title_sort | design of intrusion detection system for internet of things based on improved bp neural network |
| topic | Intrusion detection system KDD CUP 99 dataset LM-BP neural network model |
| url | https://ieeexplore.ieee.org/document/8766804/ |
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