Comparative study of CNN and RNN for deep learning based intrusion detection system

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Conference Proceeding
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2018, 11067 LNCS pp. 159 - 170
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10.1007@978-3-030-00018-915.pdfPublished version1.91 MB
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© Springer Nature Switzerland AG 2018. Intrusion detection system plays an important role in ensuring information security, and the key technology is to accurately identify various attacks in the network. Due to huge increase in network traffic and different types of attacks, accurately classifying the malicious and legitimate network traffic is time consuming and computational intensive. Recently, more and more researchers applied deep neural networks (DNNs) to solve intrusion detection problems. Convolutional Neural Network (CNN) and Recurrent Neural Network (RNN), the two main types of DNN architectures, are widely explored to enhance the performance of intrusion detection system. In this paper, we made a systematic comparison of CNN and RNN on the deep learning based intrusion detection systems, aiming to give basic guidance for DNN selection.
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