Traditional network traffic detection methods based on machine learning have been widely used, but the accuracy of identifying and detecting abnormal traffic is not high. Compared with machine learning methods, deep learning has made significant progress in fields such as text processing, video generation, and other fields, bringing new ideas to abnormal traffic detection. This paper adopts an abnormal traffic classification method based on multi-pooling cascaded convolutional neural network (MPCNN). The accuracy of extensive experiments is 0.9914, the precision is 0.9984, and the F1 score is 0.9962. These experiments achieved the company's goal of accuracy greater than 0.98. It is used for real-time monitoring of daily network traffic to recognize abnormal traffic virtually, alert topic timely, and prevent potential network threats.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Abnormal Network Traffic Detection Based on Multi-Pooling Cascaded MPCNN


    Contributors:
    He, Kefeng (author) / Shi, Yarong (author)


    Publication date :

    2025-01-17


    Size :

    681058 byte




    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English




    Railway traffic pooling scheme

    Engineering Index Backfile | 1932


    Multi-mode integrated traffic abnormal event detection method

    LI JINGLIN / LUO GUIYANG / YUAN QUAN et al. | European Patent Office | 2024

    Free access

    Cascaded Segmentation-Detection Networks for Text-Based Traffic Sign Detection

    Zhu, Yingying / Liao, Minghui / Yang, Mingkun et al. | IEEE | 2018