Applying convolutional neural network for network intrusion detection

By R. Vinayakumar, Kritik Soman, Prabaharan Poornachandran. International Conference on Advances in Computing, Communications and Informatics, 2017

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A survey of the recent architectures of deep convolutional neural networks (2019, methodology): “Due to the good feature extraction ability and efficient computations with the limited number of parameters, the trend of using 1D-CNNs is being promoted for sequential data (Vinayakumar et al. 2017; Madrazo et al. 2019).”
A Survey on Machine Learning Techniques for Cyber Security in the Last Decade (2020, methodology): “CNN is widely being used in image recognition [242], drug discovery [243], and anomaly detection [244], [245], to name a few.”
An Intrusion Detection Model Based on Feature Reduction and Convolutional Neural Networks (2019, methodology): “Finally, we compare the detection performance of the CNN–IDS model with those of the DNN and RNN models [34]–[36], all of which use the same dataset AE (100).”
Lucid: A Practical, Lightweight Deep Learning Solution for DDoS Attack Detection (2020, background): “code analysis [18], network traffic analysis [4], [19]–[21] and intrusion detection in industrial control systems [22].”