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A multi-channel anomaly detection method with feature selection and multi-scale analysis

机译：具有特征选择和多尺度分析的多通道异常检测方法

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摘要

This paper proposes a novel multi-channel network traffic anomaly detection method combined with the idea of multi-scale decomposition, feature selection and fusion. Our method includes a feature selection and fusion module, which extracts the most important features from redundancy traffic features and fuses the selected features, a multi-scale decomposition module, and a multi-channel Generalized Likelihood Ratio Test (GLRT) detection module, for anomaly detection and decision-making. The advantage of our method is that, first, it only considers the selected features and reduces the amount of computation. Second, compared with traditional anomaly detection methods that usually work on each scale independently thus mainly focused on temporally correlated traffic, our method fully explores the internal frequency-time correlations within multiple scales. It can be verified with experiments that this method performs better than other traditional methods, thus gives a new sight on the anomaly detection with different types of traffic data.
机译：本文提出了一种新型多通道网络流量异常检测方法，结合多尺度分解，特征选择和融合的思想。我们的方法包括一个特征选择和融合模块，其从冗余流量特征中提取最重要的特征，并融合所选功能，多尺度分解模块和多通道广义似然比测试（GLRT）检测模块，用于异常检测和决策。我们方法的优点是，首先，它仅考虑所选功能并减少计算量。其次，与传统的异常检测方法相比，通常独立地在每个等级上工作的方法，从而主要集中在时间上相关的流量，我们的方法完全探讨了多个尺度内的内部频率时间相关性。可以通过实验来验证，该方法比其他传统方法更好地表现更好，因此在具有不同类型的交通数据的异常检测中给出了新的景象。

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  • 来源
    《Computer networks》 |2021年第11期|107645.1-107645.10|共10页
  • 作者

    Huang Lisheng; Ran Jinye; Wang Wenyong; Yang Tan; Xiang Yu;

  • 作者单位

    Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 61173 Peoples R China;

    China Merchants Bank Operat Management Dept Shenzhen 518040 Peoples R China;

    Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 61173 Peoples R China;

    Beijing Univ Posts & Telecommun Sch Software Engn Beijing 100876 Peoples R China;

    Univ Elect Sci & Technol China Sch Comp Sci & Engn Chengdu 61173 Peoples R China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Network traffic; Multi-channel GLRT; Anomaly detection; EEMD; Feature selection;

    机译：网络流量;多通道GLRT;异常检测;EEMD;特征选择;

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