Evaluating indirect and direct classification techniques for network intrusion detection
Author:
Affiliation:
1. Department of Computer Science and Engineering, Florida Atlantic University, Boca Raton, FL, USA
2. Math and Computer Science Department, Eastern Connecticut State University, CT 06226, USA. E-mail: gaok@easternct.edu
Publisher
IOS Press
Subject
Artificial Intelligence,Computer Vision and Pattern Recognition,Theoretical Computer Science
Cited by 7 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. Network Intrusion Detection with Two-Phased Hybrid Ensemble Learning and Automatic Feature Selection;IEEE Access;2023
2. Multilayer Machine Learning-Based Intrusion Detection System;Bio-inspiring Cyber Security and Cloud Services: Trends and Innovations;2014
3. Multi-agent Artificial Immune System for Network Intrusion Detection and Classification;Advances in Intelligent Systems and Computing;2014
4. MAXIMUM VOLUME OUTLIER DETECTION AND ITS APPLICATIONS IN CREDIT RISK ANALYSIS;International Journal on Artificial Intelligence Tools;2013-10
5. Machine Learning Techniques for Anomalies Detection and Classification;Communications in Computer and Information Science;2013
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