Improving Network Traffic Anomaly Detection through Data Denoising and Unsupervised Learning
Author:
Affiliation:
1. Seattle University,Department of Computer Science,Seattle,USA
2. Amazon AWS Lambda,Seattle,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10426790/10427556/10427699.pdf?arnumber=10427699
Reference23 articles.
1. Combining Unsupervised Approaches for Near Real-Time Network Traffic Anomaly Detection
2. An efficient hybrid system for anomaly detection in social networks
3. How to introduce expert feedback in one-class support vector machines for anomaly detection?
4. An Improved KNN-Based Efficient Log Anomaly Detection Method with Automatically Labeled Samples
5. K-Means+ID3: A Novel Method for Supervised Anomaly Detection by Cascading K-Means Clustering and ID3 Decision Tree Learning Methods
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