Using Convolutional Neural Networks to Network Intrusion Detection for Cyber Threats

被引：0
|
作者：
Lin, Wen-Hui [1 ]
Lin, Hsiao-Chung [1 ]
Wang, Ping [1 ]
Wu, Bao-Hua [1 ]
Tsai, Jeng-Ying [1 ]
机构：
[1] Kun Shan Univ, Dept Informat Management, Tainan, Taiwan
来源：
PROCEEDINGS OF 4TH IEEE INTERNATIONAL CONFERENCE ON APPLIED SYSTEM INNOVATION 2018 ( IEEE ICASI 2018 ) | 2018年
关键词：
Intrusion detection; Deep Learning; Convolutional neural networks; Behavior features; LeNet-5;
D O I：
暂无
中图分类号：
TM [电工技术]; TN [电子技术、通信技术];
学科分类号：
0808 ; 0809 ;
摘要：
In practice, Defenders need a more efficient network detection approach which has the advantages of quick-responding learning capability of new network behavioural features for network intrusion detection purpose. In many applications the capability of Deep Learning techniques has been confirmed to outperform classic approaches. Accordingly, this study focused on network intrusion detection using convolutional neural networks (CNNs) based on LeNet-5 to classify the network threats. The experiment results show that the prediction accuracy of intrusion detection goes up to 99.65% with samples more than 10,000. The overall accuracy rate is 97.53%.
引用
收藏
页码：1107 / 1110
页数：4
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