Hierarchical Long Short-Term Memory Network for Cyberattack Detection
With the continuous development of network technology, cyberattack detection mechanisms play a vital role in ensuring the security of computers and network systems. However, with the rapid growth of network traffic, traditional intrusion detection systems (IDSs) are far from being able to quickly an...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/9050476/ |
| _version_ | 1818446716328738816 |
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| author | Haixia Hou Yingying Xu Menghan Chen Zhi Liu Wei Guo Mingcheng Gao Yang Xin Lizhen Cui |
| author_facet | Haixia Hou Yingying Xu Menghan Chen Zhi Liu Wei Guo Mingcheng Gao Yang Xin Lizhen Cui |
| author_sort | Haixia Hou |
| collection | DOAJ |
| description | With the continuous development of network technology, cyberattack detection mechanisms play a vital role in ensuring the security of computers and network systems. However, with the rapid growth of network traffic, traditional intrusion detection systems (IDSs) are far from being able to quickly and accurately identify complex and diverse network attacks, especially those related to low-frequency attacks. To enhance the overall security of the Internet, an IDS based on hierarchical long short-term memory (HLSTM) networks is proposed. With the introduction of HLSTM, the network can learn across multiple levels of temporal hierarchy over complex network traffic sequences. The system is evaluated on the well-known benchmark data set NSL-KDD for comparison with other existing methods. The experimental results demonstrate that compared with existing start-of-the-art methods, our system has better detection performance for different types of cyberattacks. In addition, the low-frequency network attack types have higher classification accuracy and a lower false detection rate. |
| first_indexed | 2024-12-14T19:52:09Z |
| format | Article |
| id | doaj.art-99138aaebffd44c5bedb380da2e8fca1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T19:52:09Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-99138aaebffd44c5bedb380da2e8fca12022-12-21T22:49:23ZengIEEEIEEE Access2169-35362020-01-018909079091310.1109/ACCESS.2020.29839539050476Hierarchical Long Short-Term Memory Network for Cyberattack DetectionHaixia Hou0Yingying Xu1https://orcid.org/0000-0003-2957-4481Menghan Chen2Zhi Liu3https://orcid.org/0000-0002-7640-5982Wei Guo4https://orcid.org/0000-0002-8124-5186Mingcheng Gao5Yang Xin6https://orcid.org/0000-0002-9706-3950Lizhen Cui7https://orcid.org/0000-0002-8262-8883State Key Laboratory of Networking and Switching Technology, Information Security Center, Beijing University of Posts and Telecommunications, Beijing, ChinaSchool of Information Science and Engineering, Shandong University, Qingdao, ChinaSchool of Information Science and Engineering, Shandong University, Qingdao, ChinaSchool of Information Science and Engineering, Shandong University, Qingdao, ChinaJoint SDU-NTU Center for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, ChinaState Key Laboratory of Networking and Switching Technology, Information Security Center, Beijing University of Posts and Telecommunications, Beijing, ChinaState Key Laboratory of Networking and Switching Technology, Information Security Center, Beijing University of Posts and Telecommunications, Beijing, ChinaJoint SDU-NTU Center for Artificial Intelligence Research (C-FAIR), Shandong University, Jinan, ChinaWith the continuous development of network technology, cyberattack detection mechanisms play a vital role in ensuring the security of computers and network systems. However, with the rapid growth of network traffic, traditional intrusion detection systems (IDSs) are far from being able to quickly and accurately identify complex and diverse network attacks, especially those related to low-frequency attacks. To enhance the overall security of the Internet, an IDS based on hierarchical long short-term memory (HLSTM) networks is proposed. With the introduction of HLSTM, the network can learn across multiple levels of temporal hierarchy over complex network traffic sequences. The system is evaluated on the well-known benchmark data set NSL-KDD for comparison with other existing methods. The experimental results demonstrate that compared with existing start-of-the-art methods, our system has better detection performance for different types of cyberattacks. In addition, the low-frequency network attack types have higher classification accuracy and a lower false detection rate.https://ieeexplore.ieee.org/document/9050476/HLSTMcyberattack detectionNSL-KDD data setintrusion detection system |
| spellingShingle | Haixia Hou Yingying Xu Menghan Chen Zhi Liu Wei Guo Mingcheng Gao Yang Xin Lizhen Cui Hierarchical Long Short-Term Memory Network for Cyberattack Detection IEEE Access HLSTM cyberattack detection NSL-KDD data set intrusion detection system |
| title | Hierarchical Long Short-Term Memory Network for Cyberattack Detection |
| title_full | Hierarchical Long Short-Term Memory Network for Cyberattack Detection |
| title_fullStr | Hierarchical Long Short-Term Memory Network for Cyberattack Detection |
| title_full_unstemmed | Hierarchical Long Short-Term Memory Network for Cyberattack Detection |
| title_short | Hierarchical Long Short-Term Memory Network for Cyberattack Detection |
| title_sort | hierarchical long short term memory network for cyberattack detection |
| topic | HLSTM cyberattack detection NSL-KDD data set intrusion detection system |
| url | https://ieeexplore.ieee.org/document/9050476/ |
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