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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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/

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spelling	doaj-56b399b0f5054faeae5666fcd8746f3d2021-03-30T15:21:15ZengIEEEIEEE Access2169-35362021-01-019385223853010.1109/ACCESS.2021.30642009371708Entropy-Defined Direct Batch Growing Hierarchical Self-Organizing Mapping for Efficient Network Anomaly DetectionXiaofei Qu0https://orcid.org/0000-0003-3373-9306Lin Yang1Kai Guo2Zhisong Pan3Tao Feng4Shuangyin Ren5Meng Sun6https://orcid.org/0000-0002-7435-3752Beijing Institute of Remote Sensing Information, Beijing, ChinaNational Key Laboratory of Science and Technology on Information System Security, Institute of Systems Engineering, Academy of Military Science (AMS), Beijing, ChinaNational Key Laboratory of Science and Technology on Information System Security, Institute of Systems Engineering, Academy of Military Science (AMS), Beijing, ChinaCollege of Command and Control Engineering, Army Engineering University of PLA, Nanjing, ChinaNational Key Laboratory of Science and Technology on Information System Security, Institute of Systems Engineering, Academy of Military Science (AMS), Beijing, ChinaNational Key Laboratory of Science and Technology on Information System Security, Institute of Systems Engineering, Academy of Military Science (AMS), Beijing, ChinaCollege of Command and Control Engineering, Army Engineering University of PLA, Nanjing, ChinaThis 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 dataset, that is, follow a data-adaptive manner, the proposed model is naturally valid in all cases with various data types. For fine-grained data distinguishing, a resemble entropy parameter is proposed for the first time to our best knowledge. The experimental results validate that the proposed model achieves a more efficient network anomaly detection than the conventional models, especially for real-world applications with unexpected anomaly data updating.https://ieeexplore.ieee.org/document/9371708/Anomaly detectiontopologydirect batch growing hierarchical self-organizing mapping (DBGHSOM)entropyresemble entropy
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Xiaofei Qu Lin Yang Kai Guo Zhisong Pan Tao Feng Shuangyin Ren Meng Sun
spellingShingle	Xiaofei Qu Lin Yang Kai Guo Zhisong Pan Tao Feng Shuangyin Ren Meng Sun Entropy-Defined Direct Batch Growing Hierarchical Self-Organizing Mapping for Efficient Network Anomaly Detection IEEE Access Anomaly detection topology direct batch growing hierarchical self-organizing mapping (DBGHSOM) entropy resemble entropy
author_facet	Xiaofei Qu Lin Yang Kai Guo Zhisong Pan Tao Feng Shuangyin Ren Meng Sun
author_sort	Xiaofei Qu
title	Entropy-Defined Direct Batch Growing Hierarchical Self-Organizing Mapping for Efficient Network Anomaly Detection
title_short	Entropy-Defined Direct Batch Growing Hierarchical Self-Organizing Mapping for Efficient Network Anomaly Detection
title_full	Entropy-Defined Direct Batch Growing Hierarchical Self-Organizing Mapping for Efficient Network Anomaly Detection
title_fullStr	Entropy-Defined Direct Batch Growing Hierarchical Self-Organizing Mapping for Efficient Network Anomaly Detection
title_full_unstemmed	Entropy-Defined Direct Batch Growing Hierarchical Self-Organizing Mapping for Efficient Network Anomaly Detection
title_sort	entropy-defined direct batch growing hierarchical self-organizing mapping for efficient network anomaly detection
publisher	IEEE
series	IEEE Access
issn	2169-3536
publishDate	2021-01-01
description	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 dataset, that is, follow a data-adaptive manner, the proposed model is naturally valid in all cases with various data types. For fine-grained data distinguishing, a resemble entropy parameter is proposed for the first time to our best knowledge. The experimental results validate that the proposed model achieves a more efficient network anomaly detection than the conventional models, especially for real-world applications with unexpected anomaly data updating.
topic	Anomaly detection topology direct batch growing hierarchical self-organizing mapping (DBGHSOM) entropy resemble entropy
url	https://ieeexplore.ieee.org/document/9371708/
work_keys_str_mv	AT xiaofeiqu entropydefineddirectbatchgrowinghierarchicalselforganizingmappingforefficientnetworkanomalydetection AT linyang entropydefineddirectbatchgrowinghierarchicalselforganizingmappingforefficientnetworkanomalydetection AT kaiguo entropydefineddirectbatchgrowinghierarchicalselforganizingmappingforefficientnetworkanomalydetection AT zhisongpan entropydefineddirectbatchgrowinghierarchicalselforganizingmappingforefficientnetworkanomalydetection AT taofeng entropydefineddirectbatchgrowinghierarchicalselforganizingmappingforefficientnetworkanomalydetection AT shuangyinren entropydefineddirectbatchgrowinghierarchicalselforganizingmappingforefficientnetworkanomalydetection AT mengsun entropydefineddirectbatchgrowinghierarchicalselforganizingmappingforefficientnetworkanomalydetection
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Entropy-Defined Direct Batch Growing Hierarchical Self-Organizing Mapping for Efficient Network Anomaly Detection

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