Paper Info
Title
Using Long-Short-Term Memory Based Convolutional Neural Networks for Network Intrusion Detection.
Abstract
The quantity of internet use has grown dramatically in the last decade. Internet is almost available in every human activity. However, there are some critical obstacles behind this massive development. Security becomes the hottest issue among the researchers. In this study, we focus on intrusion detection system (IDS) which is one of the solutions for security problems on network administration. Since intrusion detection system is a kind of classifier machine, it is allowed to engage with machine learning schemes. Related to this reason, the number of studies related to utilizing machine learning schemes for intrusion detection system has been increased recently. In this study, we use NSL-KDD dataset as the benchmark. Even though machine learning schemes perform well on intrusion detection, the obtained result on NSL-KDD dataset is not satisfied enough. On the other hand, deep learning offers the solution to overcome this issue. We propose two deep learning models which are long-short-term memory only (LSTM-only) and the combination of convolutional neural networks and LSTM (CNN-LSTM) for intrusion detection system. Both proposed methods achieve better accuracy than that of the existing method which uses recurrent neural networks (RNN).
Year
DOI
Venue
2018
10.1007/978-3-030-06158-6_9
WICON
Field
DocType
Citations 
Network intrusion detection,Convolutional neural network,Computer science,Long short term memory,Recurrent neural network,Artificial intelligence,Deep learning,Classifier (linguistics),Intrusion detection system,Machine learning,Distributed computing,The Internet
Conference
0
PageRank 
References 
Authors
0.34
6
5
Name
Order
Citations
PageRank
Chia-Ming Hsu100.34
He-Yen Hsieh200.68
Setya Widyawan Prakosa300.68
Muhammad Zulfan Azhari400.34
Jenq-Shiou Leu523840.64