@Article{jqc.2020.010819,
AUTHOR = {Xinda Hao, Jianmin Zhou, Xueqi Shen, Yu Yang},
TITLE = {A Novel Intrusion Detection Algorithm Based on Long Short Term Memory  Network},
JOURNAL = {Journal of Quantum Computing},
VOLUME = {2},
YEAR = {2020},
NUMBER = {2},
PAGES = {97--104},
URL = {http://www.techscience.com/jqc/v2n2/40347},
ISSN = {2579-0145},
ABSTRACT = {In recent years, machine learning technology has been widely used for 
timely network attack detection and classification. However, due to the large 
number of network traffic and the complex and variable nature of malicious 
attacks, many challenges have arisen in the field of network intrusion detection. 
Aiming at the problem that massive and high-dimensional data in cloud 
computing networks will have a negative impact on anomaly detection, this 
paper proposes a Bi-LSTM method based on attention mechanism, which learns 
by transmitting IDS data to multiple hidden layers. Abstract information and 
high-dimensional feature representation in network data messages are used to 
improve the accuracy of intrusion detection. In the experiment, we use the public 
data set KDD-Cup 99 for verification. The experimental results show that the 
model can effectively detect unpredictable malicious behaviors under the current 
network environment, improve detection accuracy and reduce false positive rate 
compared with traditional intrusion detection methods.},
DOI = {10.32604/jqc.2020.010819}
}