Intrusion-detection system based on hybrid models: review paper
The Intrusion-detection systems (IDS) is currently one of the most important security tools. However, an IDS-based hybrid model offers better results than crime detection using the same algorithm. However, hybrid models based on conventional algorithms still face different problems. The objective of...
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| Main Authors: | , , , |
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| Format: | Conference or Workshop Item |
| Language: | en |
| Published: |
2020
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| Subjects: | |
| Online Access: | http://eprints.uthm.edu.my/6218/1/P12453_811c4422b426a632d0060d86f804cc01.pdf http://eprints.uthm.edu.my/6218/ https://doi.org/10.1088/1757-899X/917/1/012059 |
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| Summary: | The Intrusion-detection systems (IDS) is currently one of the most important security tools. However, an IDS-based hybrid model offers better results than crime detection using the same algorithm. However, hybrid models based on conventional algorithms still face different problems. The objective of this study was to provide information on the most important assumptions and limitations of close hybrid analysis based on criminal analysis and to analyze the limitations of the new machine learning algorithm (FLN) to obtain IDS-based advice. |
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