A new hybrid teaching learning based Optimization -Extreme learning Machine model based Intrusion-Detection system

被引：2

作者：

Qahatan Alsudani M. ^{[1
]}

Abbdal Reflish S.H. ^{[1
]}

Moorthy K. ^{[2
]}

Mundher Adnan M. ^{[3
,4
]}

机构：

[1] Computer Techniques Engineering Department, Faculty of Information Technology, Imam Ja'afar Al-sadiq University, Najaf

[2] Faculty of Computer Systems & Software Engineering, Universiti Malaysia Pahang, Kuantan

[3] Islamic University, Najaf

[4] Faculty of Engineering, School of Computing, Universiti Teknologi Malaysia, Johor Bahru

来源：

Mater. Today Proc. | 2023年 / 2701-2705期

关键词：

Fast Learning Network; Intrusion detection system; Optimization;

D O I：

10.1016/j.matpr.2021.07.015

中图分类号：

学科分类号：

摘要：

Currently, effective Intrusion-detection systems (IDS) still represent one of the important security tools. However, hybrid models based on the IDS achieve better results compared with intrusion detection based on a single algorithm. But even so, the hybrid models based on traditional algorithms still face different limitations. This work is focused on providing two main goals; firstly, analysis based on the main methods and limitations of the most-recent hybrid model-based on intrusion detection, secondly, to propose a novel hybrid IDS model called TLBO-ELM based on the Firefly algorithm and Fast Learning Network. © 2021

引用

下载

页码：2701 / 2705

页数：4

共 50 条

[21] Methodology for the Detection of Contaminated Training Datasets for Machine Learning-Based Network Intrusion-Detection Systems
Medina-Arco, Joaquin Gaspar
Magan-Carrion, Roberto
Rodriguez-Gomez, Rafael Alejandro
Garcia-Teodoro, Pedro
SENSORS, 2024, 24 (02)
[22] CMShark: A NetFlow and machine-learning based crypto-jacking intrusion-detection method
Danesh, Hamed
Karimi, Mohammad Bagher
Arasteh, Bahman
Intelligent Decision Technologies, 2024, 18 (03): : 2255 - 2273
[23] Effective intrusion detection model through the combination of a signature-based intrusion detection system and a machine learning-based intrusion detection system
Weon, Ill-Young
Song, Doo Heon
Lee, Chang-Hoon
JOURNAL OF INFORMATION SCIENCE AND ENGINEERING, 2006, 22 (06) : 1447 - 1464
[24] Hybrid machine learning approach based intrusion detection in cloud: A metaheuristic assisted model
Mohan, Murali, V
Balajee, R. M.
Mewada, Hiren K.
Rajakumar, B. R.
Binu, D.
MULTIAGENT AND GRID SYSTEMS, 2022, 18 (01) : 21 - 43
[25] A Hybrid Intrusion Detection System Based on Machine Learning under Differential Privacy Protection
Shi, Jibo
Lin, Yun
Zhang, Zherui
Yu, Shui
2021 IEEE 94TH VEHICULAR TECHNOLOGY CONFERENCE (VTC2021-FALL), 2021,
[26] Anomaly-based intrusion detection system in IoT using kernel extreme learning machine
Bacha, Sawssen
Aljuhani, Ahamed
Abdellafou, Khawla Ben
Taouali, Okba
Liouane, Noureddine
Alazab, Mamoun
Journal of Ambient Intelligence and Humanized Computing, 2024, 15 (01) : 231 - 242
[27] Anomaly-based intrusion detection system in IoT using kernel extreme learning machine
Bacha S.
Aljuhani A.
Abdellafou K.B.
Taouali O.
Liouane N.
Alazab M.
Journal of Ambient Intelligence and Humanized Computing, 2024, 15 (1) : 231 - 242
[28] A Novel Intrusion Detection System based on Extreme Machine Learning and Multi-Voting Technology
Gao, Jianlei
Chai, Senchun
Zhang, Chen
Zhang, Baihai
Cui, Lingguo
PROCEEDINGS OF THE 38TH CHINESE CONTROL CONFERENCE (CCC), 2019, : 8909 - 8914
[29] A hybrid machine learning model for intrusion detection in VANET
Bangui, Hind
Ge, Mouzhi
Buhnova, Barbora
COMPUTING, 2022, 104 (03) : 503 - 531
[30] HYBRID MACHINE LEARNING TECHNIQUE FOR INTRUSION DETECTION SYSTEM
Tahir, Hatim Mohamad
Hasan, Wael
Said, Abas Md
Zakaria, Nur Haryani
Katuk, Norliza
Kabir, Nur Farzana
Omar, Mohd Hasbullah
Ghazali, Osman
Yahya, Noor Izzah
PROCEEDINGS OF THE 5TH INTERNATIONAL CONFERENCE ON COMPUTING & INFORMATICS, 2015, : 464 - 472

← 1 2 3 4 5 →