Intrusion-detection system based on hybrid models: review paper

Badran, Mohammed Falih and Md. Sahar, Nan and Sari, Suhaila and Taujuddin, N. S. A. M. (2020) Intrusion-detection system based on hybrid models: review paper. In: International Conference on Technology, Engineering and Sciences (ICTES) 2020, 18 April 2020, Penang, Malaysia.

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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.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: Extreme learning machine; fast learning network; intrusion detection system; optimization
Subjects: T Technology > TJ Mechanical engineering and machinery > TJ210.2-211.47 Mechanical devices and figures. Automata. Ingenious mechanisms. Robots (General)
Depositing User: Mr. Abdul Rahim Mat Radzuan
Date Deposited: 31 Jan 2022 07:07
Last Modified: 31 Jan 2022 07:07

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