In the paper, we demonstrate novel approach for network Intrusion Detection System (IDS) for cyber security using unsupervised Deep Learning (DL) techniques. Very often, the supervised learning and rules based approach like SNORT fetch problem to identify new type of attacks. In this implementation, the input samples are numerical encoded and applied un-supervised deep learning techniques called Auto Encoder (AE) and Restricted Boltzmann Machine (RBM) for feature extraction and dimensionality reduction. Then iterative k-means clustering is applied for clustering on lower dimension space with only 3 features. In addition, Unsupervised Extreme Learning Machine (UELM) is used for network intrusion detection in this implementation. We have experimented on KDD-99 dataset, the experimental results show around 91.86% and 92.12% detection accuracy using unsupervised deep learning technique AE and RBM with K-means respectively. The experimental results also demonstrate, the proposed approach shows around 4.4% and 2.95% improvement of detection accuracy using RBM with K-means against only K-mean clustering and Unsupervised Extreme Learning Machine (USELM) respectively.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Network intrusion detection for cyber security using unsupervised deep learning approaches


    Beteiligte:


    Erscheinungsdatum :

    2017-06-01


    Format / Umfang :

    327261 byte





    Medientyp :

    Aufsatz (Konferenz)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    An Avionics Cyber Intrusion Detection System

    Ryon, Luke / Rice, Greg / Potts, James | AIAA | 2020


    AN AVIONICS CYBER INTRUSION DETECTION SYSTEM... eese

    Ryon, Luke / Rice, Greg / Potts, James | TIBKAT | 2020


    Unsupervised Network Intrusion Detection System for AVTP in Automotive Ethernet Networks

    Alkhatib, Natasha / Mushtaq, Maria / Ghauch, Hadi et al. | IEEE | 2022



    Unsupervised encoder-decoder neural network security event detection

    KORAL YARON / ZHANG RENSHENG WANG / NOEL ERIC et al. | Europäisches Patentamt | 2022

    Freier Zugriff