The regular discovery of new attacks on 802.11 wireless devices emphasizes the importance of developing ML-based intrusion detection systems that generalize to such attacks. Class-imbalance issues in intrusion datasets pose a learning problem for ML solutions. Our methodology directly incorporates the class-imbalance issues to learn better feature importance measures. With features extracted using this technique from the AWID dataset, we use a gradient-boosted model to show that these features are necessary to generalize to new attack types in the AWID test dataset.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    A Gradient Boosted ML Approach to Feature Selection for Wireless Intrusion Detection


    Beteiligte:
    Mondal, Birupaxha (Autor:in) / Faisal, Fahim (Autor:in) / Tusnia Towshi, Zeba (Autor:in) / Fahad Monir, Md (Autor:in) / Ahmed, Tarem (Autor:in)


    Erscheinungsdatum :

    01.06.2023


    Format / Umfang :

    1073007 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Feature Selection for Intrusion Detection with Neural Networks and Support Vector Machines

    Mukkamala, Srinivas / Sung, Andrew H. | Transportation Research Record | 2003


    A statistical approach to predict flight delay using gradient boosted decision tree

    Manna, Suvojit / Biswas, Sanket / Kundu, Riyanka et al. | IEEE | 2017


    Genetic algorithm based feature selection algorithm for effective intrusion detection in cloud networks

    Kannan, A. / Maguire, G.Q. / Sharma, A. et al. | BASE | 2012

    Freier Zugriff