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Advanced Hybrid Information Processing. Third EAI International Conference, ADHIP 2019, Nanjing, China, September 21–22, 2019, Proceedings, Part I

Research Article

Research on the Classification Method of Network Abnormal Data

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  • @INPROCEEDINGS{10.1007/978-3-030-36402-1_27,
        author={Bozhong Liu},
        title={Research on the Classification Method of Network Abnormal Data},
        proceedings={Advanced Hybrid Information Processing. Third EAI International Conference, ADHIP 2019, Nanjing, China, September 21--22, 2019, Proceedings, Part I},
        proceedings_a={ADHIP},
        year={2019},
        month={11},
        keywords={Network anomaly Data classification Detection method Improved design},
        doi={10.1007/978-3-030-36402-1_27}
    }
    
  • Bozhong Liu
    Year: 2019
    Research on the Classification Method of Network Abnormal Data
    ADHIP
    Springer
    DOI: 10.1007/978-3-030-36402-1_27
Bozhong Liu1,*
  • 1: School of Electronic and Information Engineering
*Contact email: liubozhong77@163.com

Abstract

As people use the network more and more and release more and more personal information to the Internet, it also caused the leakage of personal information. According to the above background, the optimization research on the classification detection method of network anomaly data was proposed. Correlation analysis was carried out for the conventional algorithm, and the related model was constructed. A new algorithm was proposed to detect the network anomaly data to improve the processing ability of the network anomaly data. The experimental data showed that the proposed network anomaly data classification detection optimization algorithm improved the processing range by 31% when processing abnormal data, and the efficiency of processing data was increased by 36%. It proved the effectiveness of the new method and provided a theoretical basis for the processing of future abnormal data.

Keywords
Network anomaly Data classification Detection method Improved design
Published
2019-11-29
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-030-36402-1_27
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