Anomaly Detection System for Network Traffic using Data Mining: Machine Learning Perspective - Sandeep Chaurasia,Ruby Sharma
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Anomaly detection using Density Maximization Fuzzy C-means Algorithm: The rationale for the anomaly detection system using density maximization approach to the fuzzy c-means clustering algorithm. The workflow of a proposed anomaly detection system with density maximization FCM algorithm. The framework of ensemble classifier-based anomaly detection - this approach of anomalous detection is based on the integ ... Full description
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Description
Anomaly detection using Density Maximization Fuzzy C-means Algorithm: The rationale for the anomaly detection system using density maximization approach to the fuzzy c-means clustering algorithm. The workflow of a proposed anomaly detection system with density maximization FCM algorithm. The framework of ensemble classifier-based anomaly detection - this approach of anomalous detection is based on the integration of multiple classifiers so that the weakness of one classifier can be compensated by the other classifier. The workflow of the proposed intrusion detection framework based on an ensemble classifier.
More Information
| Author | Sandeep Chaurasia, Ruby Sharma |
|---|---|
| Publisher | LAP LAMBERT Academic Publishing |
| Release year | 2021 |
| Cover type | Softcover |
| EAN | 9786203305234 |