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- W2980609871 abstract "In this study, we present a detailed analysis of deep learning techniques for intrusion detection. Specifically, we analyze seven deep learning models, including, deep neural networks, recurrent neural networks, convolutional neural networks, restricted Boltzmann machine, deep belief networks, deep Boltzmann machines, and deep autoencoders. For each deep learning model, we study the performance of the model in binary classification and multiclass classification. We use the CSE-CIC-IDS 2018 dataset and TensorFlow system as the benchmark dataset and software library in intrusion detection experiments. In addition, we use the most important performance indicators, namely, accuracy, detection rate, and false alarm rate for evaluating the efficiency of several methods." @default.
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- W2980609871 date "2019-01-01" @default.
- W2980609871 modified "2023-10-18" @default.
- W2980609871 title "Deep Learning Techniques for Cyber Security Intrusion Detection : A Detailed Analysis" @default.
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- W2980609871 doi "https://doi.org/10.14236/ewic/icscsr19.16" @default.
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