Implementing a network intrusion detection system using semi-supervised support vector machine and random forest
Author:
Affiliation:
1. North Carolina Agricultural and Technical State University
2. Military College
Funder
National Science Foundation
Publisher
ACM
Link
https://dl.acm.org/doi/pdf/10.1145/3409334.3452073
Reference14 articles.
1. M. M. Adankon and M. Cheriet. 2010. Genetic Algorithm-based Training for Semi-supervised SVM. Neural Comput Applic (2010) 19 1197--1206 10.1007/s00521-010-0358-8 M. M. Adankon and M. Cheriet. 2010. Genetic Algorithm-based Training for Semi-supervised SVM. Neural Comput Applic (2010) 19 1197--1206 10.1007/s00521-010-0358-8
2. IDS Using Machine Learning - Current State of Art and Future Directions
3. Decision Tree Based Intrusion Detection System for NSL-KDD Dataset
4. An Intrusion Detection Model based on a Convolutional Neural Network
5. Semi-Supervised Random Forests
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