Current Result Document :
| 한글제목(Korean Title) |
A Deep Learning Approach for Intrusion Detection using Convolutional Neural Network |
| 영문제목(English Title) |
A Deep Learning Approach for Intrusion Detection using Convolutional Neural Network |
| 저자(Author) |
Seung-Ryong Lyu
Tanjung Dion
Dong-Hyun Kim
Jong-Deok Kim
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| 원문수록처(Citation) |
VOL 48 NO. 02 PP. 0730 ~ 0732 (2021. 12) |
한글내용
(Korean Abstract) |
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영문내용
(English Abstract) |
An Intrusion Detection System (IDS) plays an essential role in monitoring network traffic for the data communication system. The network Intrusion detection model protects the computer networks from unauthorized users, including insiders. Recently, various studies related to machine learning are being conducted concerning intrusion detection systems. This paper presents the deep learning model, training a network NSL-KDD dataset through Convolution Neural Networks (CNN). Through this training model, we classified the network attack types with a provided dataset.
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| 키워드(Keyword) |
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| 파일첨부 |
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