Network Intrusion Detection with 1D Convolutional Neural Networks


Hooshmand, M. K., & Huchaiah, M. D. (2022). Network Intrusion Detection with 1D Convolutional Neural Networks. Digital Technologies Research and Applications, 1(2), 66–75.


  • Mohammad Kazim Hooshmand
    Department of Computer Science, Mangalore University, Mangalore, India; Department of Computer Science, Kabul Education University, Kabul, Afghanistan
  • Manjaiah Doddaghatta Huchaiah Department of Computer Science, Mangalore University, Mangalore, India

Computer network assets expose to various cyber threats in today’s digital era. Network Anomaly Detection Systems (NADS) play a vital role in protecting digital assets in the purview of network security. Intrusion detection systems data are imbalanced and high dimensioned, affecting models’ performance in classifying malicious traffic. This paper uses a denoising autoencoder (DAE) for feature selection to reduce data dimension. To balance the data, the authors use a combined approach of oversampling technique, adaptive synthetic (ADASYN) and a cluster-based under-sampling method using a clustering algorithm, Kmeans. Then, a one-dimensional convolutional neural network (1D-CNN) is used to perform classification. The performance of the proposed model is evaluated on UNSW-NB15 and NSL-KDD datasets. The experimental results show that the model produces a detection rate of 98.79% and 97.23% on UNSW-NB15 for binary classification and multiclass classification, respectively. In the evaluation using NSL-KDD, the model yields a detection rate of 98.52% for binary type classification and 98.16% for multiclass type classification.


DAE ADASYN Feature selection Imbalance processing NADS Network security Deep learning CNN


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