Comparison of Random Forest, K-Nearest Neighbor, and Support Vector Machine Classifiers for Intrusion Detection System
Author:
Affiliation:
1. Nasarawa State University Keffi,Department of Computer Science,Nasarawa,Nigeria
2. Ahmadu Bello University Zaria,Department of Computer Science,Kaduna,Nigeria
3. Federal University of Technology Minna,Department of Computer Science,Niger,Nigeria
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10629651/10629654/10629939.pdf?arnumber=10629939
Reference16 articles.
1. Intrusion Detection using Machine Learning Techniques: An Experimental Comparison
2. Voting Classifier-Based Intrusion Detection for IoT Networks
3. A Hybrid Deep Random Neural Network for Cyberattack Detection in the Industrial Internet of Things
4. Comparative analysis of ml classifiers for network intrusion detection;Mahfouz;ICICT,2019
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