CBFS: A Clustering-Based Feature Selection Mechanism for Network Anomaly Detection

CBFS: A Clustering-Based Feature Selection Mechanism for Network Anomaly Detection

Network traffic flows contain a large number of correlated and redundant features that significantly degrade the performance of data-driven network anomaly detection. In this paper, we propose a novel clustering and ranking-based feature selection scheme, termed as CBFS, to reduce redundant features...

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Bibliographic Details
Main Authors: Jiewen Mao, Yongquan Hu, Dong Jiang, Tongquan Wei, Fuke Shen
Format: Article
Language:English
Published: IEEE 2020-01-01
Series:IEEE Access
Subjects:
Feature selection
clustering
information gain
classification
decision tree
intrusion detection
Online Access:https://ieeexplore.ieee.org/document/9123904/

Internet

https://ieeexplore.ieee.org/document/9123904/

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