Network Traffic Anomaly Detection Using Quantile Regression with Tolerance
Author:
Affiliation:
1. Kadir Has University,Department of Computer Engineering,Istanbul,Turkey
2. Turknet,Department of Data Science,Istanbul,Turkey
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10299690/10299691/10299728.pdf?arnumber=10299728
Reference19 articles.
1. Deep Window: An efficient method for online network traffic anomaly detection;shi;2019 IEEE 21st International Conference on High Performance Computing and Communications IEEE 17th International Conference on Smart City IEEE 5th International Conference on Data Science and Systems (HPCC/SmartCity/DSS),2019
2. Real-time network anomaly detection system using machine learning
3. An anomaly detection framework for cyber-security data
4. Deep Quantile Regression for Unsupervised Anomaly Detection in Time-Series
5. Anomaly Detection in Network Traffic Using Selected Methods of Time Series Analysis
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