Open AccessIntrusion-Detection System Based on Hybrid Models: Review Paper
Author(s) -
Mohammed falih badran,
Nan Md. Sahar,
Suhaila Sari,
Nik Shahidah Afifi Taujuddin
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/917/1/012059
Subject(s) - intrusion detection system , computer science , data mining , machine learning , hybrid system , advice (programming) , artificial intelligence , intrusion , face (sociological concept) , social science , sociology , programming language , geochemistry , geology
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.