Intrusion Detection System using Feature Selection With Clustering and Classification Machine Learning Algorithms on the UNSW-NB15 dataset-Reference-Cited by-同舟云学术

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Intrusion Detection System using Feature Selection With Clustering and Classification Machine Learning Algorithms on the UNSW-NB15 dataset

Published:2020-12-20 Issue: Volume: Page:
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Container-title:2020 International Conference on Innovation and Intelligence for Informatics, Computing and Technologies (3ICT)
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Author:

Hammad Mohamed,El-medany Wael,Ismail Yasser

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9311911/9311940/09312002.pdf?arnumber=9312002

Cited by 14 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. HybGBS: A hybrid neural network and grey wolf optimizer for intrusion detection in a cloud computing environment;Concurrency and Computation: Practice and Experience;2024-08-19

2. Exploring Machine Learning Strategies for Intrusion Detection in Wireless Sensor Networks;2024 IEEE 9th International Conference for Convergence in Technology (I2CT);2024-04-05

3. Performance Evaluation of Network Intrusion Detection Using Machine Learning;2024 IEEE International Conference on Consumer Electronics (ICCE);2024-01-06

4. The Implementation of Machine Learning for Optimizing Network-Based Intrusion Detection in the Snort Application;2023 6th International Seminar on Research of Information Technology and Intelligent Systems (ISRITI);2023-12-11

5. Enhancing Intrusion Detection System Performance to Detect Attacks on Edge of Things;SN Computer Science;2023-10-14

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