Paper Info
Title
An Intrusion Detection System Using a Deep Neural Network With Gated Recurrent Units.
Abstract
To improve the performance of network intrusion detection systems (IDS), we applied deep learning theory to intrusion detection and developed a deep network model with automatic feature extraction. In this paper, we consider the characteristics of the time-related intrusion and propose a novel IDS that consists of a recurrent neural network with gated recurrent units (GRU), multilayer perceptron (MLP), and softmax module. Experiments on the well-known KDD 99 and NSL-KDD data sets show that the system has leading performance. The overall detection rate was 99.42% using KDD 99 and 99.31% using NSL-KDD with false positive rates as low as 0.05% and 0.84%, respectively. In particular, for detecting the denial of service attacks, the system achieved detection rates of 99.98% and 99.55%, respectively. Comparative experiments showed that the GRU is more suitable as a memory unit for IDS than LSTM, and proved that it is an effective simplification and improvement of LSTM. Moreover, the bidirectional GRU can reach the best performance compared with the recently published methods.
Year
DOI
Venue
2018
10.1109/ACCESS.2018.2867564
IEEE ACCESS
Keywords
Field
DocType
Intrusion detection,deep learning,recurrent neural network,gated recurrent unit
Pattern recognition,Softmax function,Computer science,Support vector machine,Recurrent neural network,Multilayer perceptron,Artificial intelligence,Deep learning,Artificial neural network,Intrusion detection system,Network model,Distributed computing
Journal
Volume
ISSN
Citations 
6
2169-3536
3
PageRank 
References 
Authors
0.39
0
4
Name
Order
Citations
PageRank
Congyuan Xu160.84
Jizhong Shen25812.28
Xin Du312726.78
Fan Zhang49024.51