An Effective Intrusion Detection Classifier Using Long Short-Term Memory with Gradient Descent Optimization

被引:0
作者
Thi-Thu-Huong Le [1 ]
Kim, Jihyun [1 ]
Kim, Howon [1 ]
机构
[1] Pusan Natl Univ, Sch Elect & Comp Engn, Busan, South Korea
来源
2017 INTERNATIONAL CONFERENCE ON PLATFORM TECHNOLOGY AND SERVICE (PLATCON) | 2017年
关键词
Deep Learning; Gradient Descent Optimization; Intrusion Detection System; Long Short-Term Memory; Optimizer;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
Intrusion Detection System (IDS) is one of the important issues in network security. IDSs are built to detect both known and unknown malicious attacks. Several machine learning algorithms are used widely in IDS such as neural network, SVM, KNN etc. However, these algorithms have still some limitations such as high false positive and false alarm rate. In this paper, our contribution is to build a classifier of IDS following deep learning approach. We find the most suitable optimizer among six optimizes for Long Short-Term Memory Recurrent Neural Network (LSTM RNN) model are used to IDS. Through our experiments, we found that LSTM RNN model with Nadam optimizer outperforms to previous works. We demonstrate our approach is really efficiency to intrusion detection with accuracy is 97.54%, detection rate is 98.95%, and the false alarm rate is reasonable with 9.98%.
引用
收藏
页码:155 / 160
页数:6
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