@Article{jqc.2021.016857,
AUTHOR = {Linbei Wang
, Zaoyu Tao, Lina Wang, Yongjun Ren},
TITLE = {A Hybrid Intrusion Detection Model Based on Spatiotemporal Features},
JOURNAL = {Journal of Quantum Computing},
VOLUME = {3},
YEAR = {2021},
NUMBER = {3},
PAGES = {107--118},
URL = {http://www.techscience.com/jqc/v3n3/46040},
ISSN = {2579-0145},
ABSTRACT = {With the accelerating process of social informatization, our personal 
information security and Internet sites, etc., have been facing a series of threats 
and challenges. Recently, well-developed neural network has seen great 
advancement in natural language processing and computer vision, which is also 
adopted in intrusion detection. In this research, a hybrid model integrating MultiScale Convolutional Neural Network and Long Short-term Memory Network
(MSCNN-LSTM) is designed to conduct the intrusion detection. Multi-Scale 
Convolutional Neural Network (MSCNN) is used to extract the spatial 
characteristics of data sets. And Long Short-term Memory Network (LSTM) is 
responsible for processing the temporal characteristics. The data set used in this 
experiment is KDDCUP99 with different probability distributions in the training 
set and test set involving some newly emerging attack types, making the data more 
realistic. As a result, this type of data set is widely applied in the simulation 
experiment of intrusion detection. In this experiment, the assessment indices such 
as the accuracy rate, recall rate and F1 score are introduced to check the 
performance of this model.},
DOI = {10.32604/jqc.2021.016857}
}