@Article{cmc.2020.010102,
AUTHOR = {Ziyong Ran, Desheng Zheng, Yanling Lai, Lulu Tian},
TITLE = {Applying Stack Bidirectional LSTM Model to Intrusion Detection},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {65},
YEAR = {2020},
NUMBER = {1},
PAGES = {309--320},
URL = {http://www.techscience.com/cmc/v65n1/39567},
ISSN = {1546-2226},
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.},
DOI = {10.32604/cmc.2020.010102}
}