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Enhanced Network Intrusion Detection using Deep Convolutional Neural Networks

Naseer, Sheraz ; Saleem, Yasir

KSII Transactions on Internet and Information Systems, 2018, 12(10), , pp.5159-5178 [Periódico revisado por pares]

한국인터넷정보학회

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  • Título:
    Enhanced Network Intrusion Detection using Deep Convolutional Neural Networks
  • Autor: Naseer, Sheraz ; Saleem, Yasir
  • Assuntos: Applied research ; Artificial neural networks ; CNN ; Data security ; Deep Convolutional Neural Networks ; Deep learning ; IDS ; Information Security ; Machine learning ; Methods ; Network Intrusion Detection ; 컴퓨터학
  • É parte de: KSII Transactions on Internet and Information Systems, 2018, 12(10), , pp.5159-5178
  • Notas: Korean Society for Internet Information
    KISTI1.1003/JNL.JAKO201835372300473
    http://doi.org/10.3837/tiis.2018.10.028
  • Descrição: Network Intrusion detection is a rapidly growing field of information security due to its importance for modern IT infrastructure. Many supervised and unsupervised learning techniques have been devised by researchers from discipline of machine learning and data mining to achieve reliable detection of anomalies. In this paper, a deep convolutional neural network (DCNN) based intrusion detection system (IDS) is proposed, implemented and analyzed. Deep CNN core of proposed IDS is fine-tuned using Randomized search over configuration space. Proposed system is trained and tested on NSLKDD training and testing datasets using GPU. Performance comparisons of proposed DCNN model are provided with other classifiers using well-known metrics including Receiver operating characteristics (RoC) curve, Area under RoC curve (AuC), accuracy, precision-recall curve and mean average precision (mAP). The experimental results of proposed DCNN based IDS shows promising results for real world application in anomaly detection systems.
  • Editor: 한국인터넷정보학회
  • Idioma: Coreano;Inglês
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