Systems and methods are provided for combining a multiple sub-time window sampling architecture with machine learning to detect outlier traffic flow behavior which may indicate malicious/problematic network activity. For example, a network device may obtain a sample of traffic flow data during a defined time window. The sample of traffic flow data may comprise information associated with a sampled subset of traffic flows transferred by a network device in the defined time window. The network device may partition the defined time window into two or more sub-time windows. In each sub-time window, using machine learning, the network device may assign an outlier-related classification to each sampled traffic flow based on the relative behavioral characteristics of all the sampled traffic flows. The network device may aggregate the outlier-related classifications for each sampled traffic flow across multiple sub-time windows, and process traffic flows based on the aggregated outlier-related classifications.


    Access

    Download


    Export, share and cite



    Title :

    Assigning outlier-related classifications to traffic flows across multiple time windows


    Contributors:

    Publication date :

    2024-07-02


    Type of media :

    Patent


    Type of material :

    Electronic Resource


    Language :

    English


    Classification :

    IPC:    G08G Anlagen zur Steuerung, Regelung oder Überwachung des Verkehrs , TRAFFIC CONTROL SYSTEMS



    ASSIGNING OUTLIER-RELATED CLASSIFICATIONS TO TRAFFIC FLOWS ACROSS MULTIPLE TIME WINDOWS

    SESHA MADHUSOODHANA CHARI / SUKUMARAN SUNIL | European Patent Office | 2023

    Free access

    Assigning user class link flows uniquely

    Boyce, David / Xie, Jun | Elsevier | 2013


    Assigning user class link flows uniquely

    Boyce, David | Online Contents | 2013


    Assigning Traffic Over Essentially-Least-Cost Paths

    Hunt, David T. | Online Contents | 1996