Open Access
ARTICLE
Applying Stack Bidirectional LSTM Model to Intrusion Detection
Ziyong Ran1, Desheng Zheng1, *, Yanling Lai1, Lulu Tian2
1 School of Computer Science, Research Center for Cyber Security, Southwest Petroleum University, Chengdu, 610500, China.
2 Brunel University London, Uxbridge, Middlesex, UB8 3PH, UK.
* Corresponding Author: Desheng Zheng. Email: .
Computers, Materials & Continua 2020, 65(1), 309-320. https://doi.org/10.32604/cmc.2020.010102
Received 11 February 2020; Accepted 01 May 2020; Issue published 23 July 2020
Abstract
Nowadays, Internet has become an indispensable part of daily life and is used
in many fields. Due to the large amount of Internet traffic, computers are subject to
various security threats, which may cause serious economic losses and even endanger
national security. It is hoped that an effective security method can systematically classify
intrusion data in order to avoid leakage of important data or misuse of data. As machine
learning technology matures, deep learning is widely used in various industries.
Combining deep learning with network security and intrusion detection is the current
trend. In this paper, the problem of data classification in intrusion detection system is
studied. We propose an intrusion detection model based on stack bidirectional long shortterm memory (LSTM), introduce stack bidirectional LSTM into the field of intrusion
detection and apply it to the intrusion detection. In order to determine the appropriate
parameters and structure of stack bidirectional LSTM network, we have carried out
experiments on various network structures and parameters and analyzed the experimental
results. The classic KDD Cup’1999 dataset was selected for experiments so that we can
obtain convincing and comparable results. Experimental results derived from the KDD
Cup’1999 dataset show that the network with three hidden layers containing 80 LSTM
cells is superior to other algorithms in computational cost and detection performance due
to stack bidirectional LSTM model’s ability to review time and correlate with connected
records continuously. The experiment shows the effectiveness of stack bidirectional
LSTM network in intrusion detection.
Keywords
Cite This Article
APA Style
Ran, Z., Zheng, D., Lai, Y., Tian, L. (2020). Applying stack bidirectional LSTM model to intrusion detection. Computers, Materials & Continua, 65(1), 309-320. https://doi.org/10.32604/cmc.2020.010102
Vancouver Style
Ran Z, Zheng D, Lai Y, Tian L. Applying stack bidirectional LSTM model to intrusion detection. Comput Mater Contin. 2020;65(1):309-320 https://doi.org/10.32604/cmc.2020.010102
IEEE Style
Z. Ran, D. Zheng, Y. Lai, and L. Tian "Applying Stack Bidirectional LSTM Model to Intrusion Detection," Comput. Mater. Contin., vol. 65, no. 1, pp. 309-320. 2020. https://doi.org/10.32604/cmc.2020.010102
Citations