Intrusive Detection in Nuclear Systems using Supervised and Unsupervised M1 Methodologies
Author:
Affiliation:
1. Saveetha Engineering College,Department of Electronics and Communication Engineering,Chennai,India
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10560063/10560071/10560278.pdf?arnumber=10560278
Reference15 articles.
1. Deep and Machine Learning Approaches for Anomaly-Based Intrusion Detection of Imbalanced Network Traffic
2. A Novel Machine Learning Approach to Detect Phishing Websites
3. Mining Anomalies in Large ISCX Dataset Using Machine Learning Algorithms in KNIME
4. A Novel Distributed Machine Learning Framework for Semi-Supervised Detection of Botnet Attacks
5. Feature selection in UNSW-NB15 and KDDCUP'99 datasets
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