Combination of Feature Selection and Hybrid Classifier as to Network Intrusion Detection System Adopting FA, GWO, and BAT Optimizers-Reference-Cited by-同舟云学术

Combination of Feature Selection and Hybrid Classifier as to Network Intrusion Detection System Adopting FA, GWO, and BAT Optimizers

Published:2021-12-29 Issue: Volume: Page:
ISSN:
Container-title:2021 7th International Conference on Signal Processing and Intelligent Systems (ICSPIS)
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Author:

Alizadeh Mousa¹,Mousavi Sadegh E²,Beheshti Mohammad T H³,Ostadi Ali³

Affiliation:

1. Tarbiat Modares University,Department of Electrical and Computer Engineering,Tehran,Iran

2. Islamic Azad University, South Tehran Branch,Department of Industrial Engineering,Tehran,Iran

3. Islamic Azad University, North Tehran Branch,Department of Industrial Engineering,Tehran,Iran

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9729328/9729329/09729365.pdf?arnumber=9729365

Reference22 articles.

1. DIDDOS: An approach for detection and identification of Distributed Denial of Service (DDoS) cyberattacks using Gated Recurrent Units (GRU)

2. A deep long short-term memory based classifier for wireless intrusion detection system;sydney mambwe;ICT Express,2020

3. Network Traffic Forecasting Based on Fixed Telecommunication Data Using Deep Learning;mousa;2020 6th Iranian Conference on Signal Processing and Intelligent Systems (ICSPIS),0

4. An efficient flow-based multi-level hybrid intrusion detection system for software-defined networks;majd;CCF Transactions on Networking,2020

5. Unsupervised intelligent system based on one class support vector machine and Grey Wolf optimization for IoT botnet detection

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