DNS protocol is a plaintext domain name resolution protocol, which has the risk of privacy disclosure. DNS over HTTPS (DOH) protocol is designed to encrypt DNS traffic, which solves the privacy problem. However, many network attackers use the DOH tunnel for malicious transmission. From the passive traffic, there is no obvious difference between normal DOH traffic and DOH tunnel traffic, which brings great challenges to identify them. At present, researches mainly focus on the plaintext DNS covert tunnel, but less on the encrypted DOH tunnel. In this paper, we propose DOH covert tunnel detection method based on core features and machine learning method using two steps. Firstly, we detect DOH traffic according to the threshold of features. On this basis, we use core features and machine learning methods to detect tunnel traffic in all DOH traffic. Finally, we use self collected and public datasets to verify our method. The results show that the method achieves up to 99 % precision and recall that is superior to state of the art method.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Identifying DoH Tunnel Traffic Using Core Feathers and Machine Learning Method


    Beteiligte:
    Wang, Bingxu (Autor:in) / Xiong, Gang (Autor:in) / Gou, Gaopeng (Autor:in) / Song, Jiaying (Autor:in) / Li, Zhen (Autor:in) / Yang, Qingya (Autor:in)


    Erscheinungsdatum :

    24.05.2023


    Format / Umfang :

    1556895 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch




    Smart Tunnel Traffic Control System using IoT and Machine Learning

    V, Subramani / S, Vishnu Kumar / R, Rishikanth S et al. | IEEE | 2025


    Tunnel traffic scheduling system and tunnel traffic scheduling method

    LI SHUGUANG / DU PENGXIANG / ZHAO XIAOFEI et al. | Europäisches Patentamt | 2024

    Freier Zugriff

    TITANIUM FEATHERS FOR THE FIREBIRD II

    McLEAN, ROBERT F. | SAE | 1956


    Traffic Prediction Using Machine Learning

    Deekshetha, H. R. / Shreyas Madhav, A. V. / Tyagi, Amit Kumar | Springer Verlag | 2022