Entropy-Defined Direct Batch Growing Hierarchical Self-Organizing Mapping for Efficient Network Anomaly Detection

Entropy-Defined Direct Batch Growing Hierarchical Self-Organizing Mapping for Efficient Network Anomaly Detection

This paper proposes a network anomaly detection model of direct batch growing hierarchical self-organizing mapping based on entropy, which facilitates clear topology representation for the asymmetrically-distributed data. Since the entropy-defined parameters dynamically vary with the incident datase...

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Bibliographic Details
Main Authors: Xiaofei Qu, Lin Yang, Kai Guo, Zhisong Pan, Tao Feng, Shuangyin Ren, Meng Sun
Format: Article
Language:English
Published: IEEE 2021-01-01
Series:IEEE Access
Subjects:
Anomaly detection
topology
direct batch growing hierarchical self-organizing mapping (DBGHSOM)
entropy
resemble entropy
Online Access:https://ieeexplore.ieee.org/document/9371708/

Internet

https://ieeexplore.ieee.org/document/9371708/

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