In tactical scenarios, there is limited knowledge available about an adversary’s encrypted traffic. To improve traffic classification performance in these scenarios, a new modified naïve Bayes kernel classifier (MNBK) is proposed based on optimal weight-based kernel bandwidth selection. By generating several traffic types expected in modern tactical edge networks, we demonstrate that the proposed MNBK classifier not only improves classification performance on the existing classes, but also detects unknown traffic with very high accuracy, precision, and recall compared with the traditional classifiers. In addition, a real time learning model is proposed based on MNBK and applied to real time traffic classification.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Machine Learning-Based Traffic Classification of Wireless Traffic


    Beteiligte:
    Song, Ronggong (Autor:in) / Willink, Tricia (Autor:in)


    Erscheinungsdatum :

    01.05.2019


    Format / Umfang :

    695135 byte




    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    GPS-Based Traffic Conditions Classification Using Machine Learning Approaches

    Ahmed, Usman / Tu, Ran / Xu, Junshi et al. | Transportation Research Record | 2022


    Federal learning traffic prediction method based on traffic mode classification

    ZHANG XINMIN / SUN SHU / QIAN JINCHUAN et al. | Europäisches Patentamt | 2025

    Freier Zugriff

    Classification of Traffic Accident Severity Using Machine Learning Models

    Hamdan, Noura / Sipos, Tibor | Springer Verlag | 2025


    WIRELESS ROADSIDE MACHINE, TRAFFIC COMMUNICATION SYSTEM, AND TRAFFIC COMMUNICATION METHOD

    KONISHI TOMOAKI | Europäisches Patentamt | 2022

    Freier Zugriff