Paper Info
Title
A novel wide & deep transfer learning stacked GRU framework for network intrusion detection
Abstract
With the increasing frequency, severity and complexity of recent cyber attacks around the world, network intrusion detection has become mandatory and highly sophisticated task. Achieving high performance in network intrusion detection by applying benchmark machine learning classifiers (including deep learning techniques) has become a major challenge in recent times. One of the biggest challenges is improving the memorization capacity and generalization ability of NIDS (Network Intrusion Detection Systems). In this paper, we propose a highly scalable novel wide & deep transfer learning (TL) based stacked GRU (Gated Recurrent Unit) model to deal with multi-dimensional point data and multi-variate time series regression and classification problems in network intrusion detection. The proposed model has the memorization capacity of linear regression model and the generalization ability of deep GRU model. The deep component consists of a transfer learning framework that pretrains a source model and then fine-tunes the whole source model on the same dataset multiple times until it gives peak performance. This method gives a multi-class evaluation accuracy of 99.92% on KDDCup 99(10%) dataset and 94.22% on UNSW-NB15 dataset respectively. Extensive experimentations and evaluations have been carried out by comparing it with other machine learning (including deep learning) network intrusion detection techniques. The proposed method outperforms most of the existing intrusion detection approaches.
Year
DOI
Venue
2021
10.1016/j.jisa.2021.102899
Journal of Information Security and Applications
Keywords
DocType
Volume
Deep learning,GRU,Wide & deep,Transfer learning,NIDS
Journal
61
ISSN
Citations 
PageRank 
2214-2126
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Nongmeikapam Brajabidhu Singh100.34
Moirangthem Marjit Singh201.01
Arindam Sarkar3138.84
J. K. Mandal4138.23