Study on a Convolutional Neural Network Model for Attack Type Classification in the Field of Network Intrusion Detection

Author:

Joo SeungSaeORCID,Lee SungHyukORCID,Yu JaehakORCID,Moon DaesungORCID,Bae Ji-HoonORCID

Funder

Ministry of Science and ICT, South Korea

Institute of Information and Communications Technology Planning and Evaluation

Publisher

Korean Institute of Information Technology

Reference26 articles.

1. AD-IoT: Anomaly Detection of IoT Cyberattacks in Smart City Using Machine Learning

2. T. H. Kim and S. H. Kim, "An Intrusion Detection System based on the Artificial Neural Network for Real Time Detection", Convergence Security Journal, Vol. 17, No. 1, pp. 31-38, Mar. 2017.

3. Network Intrusion Detection System using Feature Extraction based on Deep Sparse Autoencoder

4. B. J. Min, J. H. Yoo, S. S. Kim, D. I. Shin, and D. K. Shin, "Network Intrusion Detection with One Class Anomaly Detection Model based on Auto Encoder", Journal of Internet Computing and Services, Vol. 22, Vo. 1, pp. 13-22, Feb. 2021. https://doi.org/10.7472/jksii.2021.22.1.13.

5. Network Intrusion Detection System Based on an Adversarial Auto-Encoder with Few Labeled Training Samples

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