Online Intrusion Scenario Discovery and Prediction Based on Hierarchical Temporal Memory (HTM)
With the development of intrusion detection, a number of the intelligence algorithms (e.g., artificial neural networks) are introduced to enhance the performance of the intrusion detection systems. However, many intelligence algorithms should be trained before being used, and retrained regularly, wh...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-04-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/10/7/2596 |
| _version_ | 1827718934421307392 |
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| author | Kai Zhang Fei Zhao Shoushan Luo Yang Xin Hongliang Zhu Yuling Chen |
| author_facet | Kai Zhang Fei Zhao Shoushan Luo Yang Xin Hongliang Zhu Yuling Chen |
| author_sort | Kai Zhang |
| collection | DOAJ |
| description | With the development of intrusion detection, a number of the intelligence algorithms (e.g., artificial neural networks) are introduced to enhance the performance of the intrusion detection systems. However, many intelligence algorithms should be trained before being used, and retrained regularly, which is not applicable for continuous online learning and analyzing. In this paper, a new online intrusion scenario discovery framework is proposed and the intelligence algorithm HTM (Hierarchical Temporal Memory) is employed to improve the performance of the online learning ability of the system. The proposed framework can discover and model intrusion scenarios, and the constructed model keeps evolving with the variance of the data. Additionally, a series of data preprocessing methods are introduced to enhance its adaptability to the noisy and twisted data. The experimental results show that the framework is effective in intrusion scenario discovery, and the discovered scenario is more concise and accurate than our previous work. |
| first_indexed | 2024-03-10T20:32:42Z |
| format | Article |
| id | doaj.art-2434bc9844bc4b3daf50c35e2a7843af |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T20:32:42Z |
| publishDate | 2020-04-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-2434bc9844bc4b3daf50c35e2a7843af2023-11-19T21:13:44ZengMDPI AGApplied Sciences2076-34172020-04-01107259610.3390/app10072596Online Intrusion Scenario Discovery and Prediction Based on Hierarchical Temporal Memory (HTM)Kai Zhang0Fei Zhao1Shoushan Luo2Yang Xin3Hongliang Zhu4Yuling Chen5National Engineering Laboratory for Disaster Backup and Recovery, Information Security Center, School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaNational Engineering Laboratory for Disaster Backup and Recovery, Information Security Center, School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaNational Engineering Laboratory for Disaster Backup and Recovery, Information Security Center, School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaNational Engineering Laboratory for Disaster Backup and Recovery, Information Security Center, School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaNational Engineering Laboratory for Disaster Backup and Recovery, Information Security Center, School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, ChinaGuizhou Provincial Key Laboratory of Public Big Data, Guizhou University, Guizhou 550025, ChinaWith the development of intrusion detection, a number of the intelligence algorithms (e.g., artificial neural networks) are introduced to enhance the performance of the intrusion detection systems. However, many intelligence algorithms should be trained before being used, and retrained regularly, which is not applicable for continuous online learning and analyzing. In this paper, a new online intrusion scenario discovery framework is proposed and the intelligence algorithm HTM (Hierarchical Temporal Memory) is employed to improve the performance of the online learning ability of the system. The proposed framework can discover and model intrusion scenarios, and the constructed model keeps evolving with the variance of the data. Additionally, a series of data preprocessing methods are introduced to enhance its adaptability to the noisy and twisted data. The experimental results show that the framework is effective in intrusion scenario discovery, and the discovered scenario is more concise and accurate than our previous work.https://www.mdpi.com/2076-3417/10/7/2596intrusion detectionintrusion scenario discoveryattack predictioncorrelation analysisIDS alertsHTM |
| spellingShingle | Kai Zhang Fei Zhao Shoushan Luo Yang Xin Hongliang Zhu Yuling Chen Online Intrusion Scenario Discovery and Prediction Based on Hierarchical Temporal Memory (HTM) Applied Sciences intrusion detection intrusion scenario discovery attack prediction correlation analysis IDS alerts HTM |
| title | Online Intrusion Scenario Discovery and Prediction Based on Hierarchical Temporal Memory (HTM) |
| title_full | Online Intrusion Scenario Discovery and Prediction Based on Hierarchical Temporal Memory (HTM) |
| title_fullStr | Online Intrusion Scenario Discovery and Prediction Based on Hierarchical Temporal Memory (HTM) |
| title_full_unstemmed | Online Intrusion Scenario Discovery and Prediction Based on Hierarchical Temporal Memory (HTM) |
| title_short | Online Intrusion Scenario Discovery and Prediction Based on Hierarchical Temporal Memory (HTM) |
| title_sort | online intrusion scenario discovery and prediction based on hierarchical temporal memory htm |
| topic | intrusion detection intrusion scenario discovery attack prediction correlation analysis IDS alerts HTM |
| url | https://www.mdpi.com/2076-3417/10/7/2596 |
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