Sequential Model Based Intrusion Detection System for IoT Servers Using Deep Learning Methods
IoT plays an important role in daily life; commands and data transfer rapidly between the servers and objects to provide services. However, cyber threats have become a critical factor, especially for IoT servers. There should be a vigorous way to protect the network infrastructures from various atta...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2021-02-01
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| Series: | Sensors |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1424-8220/21/4/1113 |
| _version_ | 1797414253849739264 |
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| author | Ming Zhong Yajin Zhou Gang Chen |
| author_facet | Ming Zhong Yajin Zhou Gang Chen |
| author_sort | Ming Zhong |
| collection | DOAJ |
| description | IoT plays an important role in daily life; commands and data transfer rapidly between the servers and objects to provide services. However, cyber threats have become a critical factor, especially for IoT servers. There should be a vigorous way to protect the network infrastructures from various attacks. IDS (Intrusion Detection System) is the invisible guardian for IoT servers. Many machine learning methods have been applied in IDS. However, there is a need to improve the IDS system for both accuracy and performance. Deep learning is a promising technique that has been used in many areas, including pattern recognition, natural language processing, etc. The deep learning reveals more potential than traditional machine learning methods. In this paper, sequential model is the key point, and new methods are proposed by the features of the model. The model can collect features from the network layer via tcpdump packets and application layer via system routines. Text-CNN and GRU methods are chosen because the can treat sequential data as a language model. The advantage compared with the traditional methods is that they can extract more features from the data and the experiments show that the deep learning methods have higher F1-score. We conclude that the sequential model-based intrusion detection system using deep learning method can contribute to the security of the IoT servers. |
| first_indexed | 2024-03-09T05:29:24Z |
| format | Article |
| id | doaj.art-86517358cfe24a709cf49fb502d19838 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-03-09T05:29:24Z |
| publishDate | 2021-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-86517358cfe24a709cf49fb502d198382023-12-03T12:33:37ZengMDPI AGSensors1424-82202021-02-01214111310.3390/s21041113Sequential Model Based Intrusion Detection System for IoT Servers Using Deep Learning MethodsMing Zhong0Yajin Zhou1Gang Chen2Computer Science and Technology College, Zhejiang University, Hangzhou 310027, ChinaComputer Science and Technology College, Zhejiang University, Hangzhou 310027, ChinaComputer Science and Technology College, Zhejiang University, Hangzhou 310027, ChinaIoT plays an important role in daily life; commands and data transfer rapidly between the servers and objects to provide services. However, cyber threats have become a critical factor, especially for IoT servers. There should be a vigorous way to protect the network infrastructures from various attacks. IDS (Intrusion Detection System) is the invisible guardian for IoT servers. Many machine learning methods have been applied in IDS. However, there is a need to improve the IDS system for both accuracy and performance. Deep learning is a promising technique that has been used in many areas, including pattern recognition, natural language processing, etc. The deep learning reveals more potential than traditional machine learning methods. In this paper, sequential model is the key point, and new methods are proposed by the features of the model. The model can collect features from the network layer via tcpdump packets and application layer via system routines. Text-CNN and GRU methods are chosen because the can treat sequential data as a language model. The advantage compared with the traditional methods is that they can extract more features from the data and the experiments show that the deep learning methods have higher F1-score. We conclude that the sequential model-based intrusion detection system using deep learning method can contribute to the security of the IoT servers.https://www.mdpi.com/1424-8220/21/4/1113IoTIntrusion Detection Systemsystem securitydeep learningsequential model |
| spellingShingle | Ming Zhong Yajin Zhou Gang Chen Sequential Model Based Intrusion Detection System for IoT Servers Using Deep Learning Methods Sensors IoT Intrusion Detection System system security deep learning sequential model |
| title | Sequential Model Based Intrusion Detection System for IoT Servers Using Deep Learning Methods |
| title_full | Sequential Model Based Intrusion Detection System for IoT Servers Using Deep Learning Methods |
| title_fullStr | Sequential Model Based Intrusion Detection System for IoT Servers Using Deep Learning Methods |
| title_full_unstemmed | Sequential Model Based Intrusion Detection System for IoT Servers Using Deep Learning Methods |
| title_short | Sequential Model Based Intrusion Detection System for IoT Servers Using Deep Learning Methods |
| title_sort | sequential model based intrusion detection system for iot servers using deep learning methods |
| topic | IoT Intrusion Detection System system security deep learning sequential model |
| url | https://www.mdpi.com/1424-8220/21/4/1113 |
| work_keys_str_mv | AT mingzhong sequentialmodelbasedintrusiondetectionsystemforiotserversusingdeeplearningmethods AT yajinzhou sequentialmodelbasedintrusiondetectionsystemforiotserversusingdeeplearningmethods AT gangchen sequentialmodelbasedintrusiondetectionsystemforiotserversusingdeeplearningmethods |