sesa 16(9): e2

Research Article

A Deep Learning Approach for Network Intrusion Detection System

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  • @ARTICLE{10.4108/eai.3-12-2015.2262516,
        author={Ahmad Javaid and Quamar Niyaz and Weiqing Sun and Mansoor Alam},
        title={A Deep Learning Approach for Network Intrusion Detection System},
        journal={EAI Endorsed Transactions on Security and Safety},
        volume={3},
        number={9},
        publisher={ACM},
        journal_a={SESA},
        year={2016},
        month={5},
        keywords={network security, nids, deep learning, sparse autoencoder, nsl-kdd},
        doi={10.4108/eai.3-12-2015.2262516}
    }
    
  • Ahmad Javaid
    Quamar Niyaz
    Weiqing Sun
    Mansoor Alam
    Year: 2016
    A Deep Learning Approach for Network Intrusion Detection System
    SESA
    EAI
    DOI: 10.4108/eai.3-12-2015.2262516
Ahmad Javaid1,*, Quamar Niyaz1, Weiqing Sun1, Mansoor Alam1
  • 1: University of Toledo
*Contact email: ahmad.javaid@utoledo.edu

Abstract

A Network Intrusion Detection System (NIDS) helps system administrators to detect network security breaches in their organizations. However, many challenges arise while developing a flexible and efficient NIDS for unforeseen and unpredictable attacks. We propose a deep learning based approach for developing such an efficient and flexible NIDS. We use Self-taught Learning (STL), a deep learning based technique, on NSL-KDD - a benchmark dataset for network intrusion. We present the performance of our approach and compare it with a few previous work. Compared metrics include accuracy, precision, recall, and f-measure values.