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CNID: Research of Network Intrusion Detection Based on Convolutional Neural Network

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  • Guojie Liu
  • Jianbiao Zhang

Abstract

Network intrusion detection system can effectively detect network attack behaviour, which is very important to network security. In this paper, a multiclassification network intrusion detection model based on convolutional neural network is proposed, and the algorithm is optimized. First, the data is preprocessed, the original one-dimensional network intrusion data is converted into two-dimensional data, and then the effective features are learned using optimized convolutional neural networks, and, finally, the final test results are produced in conjunction with the Softmax classifier. In this paper, KDD-CUP 99 and NSL-KDD standard network intrusion detection dataset were used to carry out the multiclassification network intrusion detection experiment; the experimental results show that the multiclassification network intrusion detection model proposed in this paper improves the accuracy and check rate, reduces the false positive rate, and also obtains better test results for the detection of unknown attacks.

Suggested Citation

  • Guojie Liu & Jianbiao Zhang, 2020. "CNID: Research of Network Intrusion Detection Based on Convolutional Neural Network," Discrete Dynamics in Nature and Society, Hindawi, vol. 2020, pages 1-11, May.
    • Handle: RePEc:hin:jnddns:4705982
    DOI: 10.1155/2020/4705982
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    Cited by:

    1. Hesham Kamal & Maggie Mashaly, 2024. "Advanced Hybrid Transformer-CNN Deep Learning Model for Effective Intrusion Detection Systems with Class Imbalance Mitigation Using Resampling Techniques," Future Internet, MDPI, vol. 16(12), pages 1-74, December.

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