Open Access
ARTICLE
A Hybrid Intrusion Detection Model Based on Spatiotemporal Features
Linbei Wang1
, Zaoyu Tao1, Lina Wang2,*, Yongjun Ren3
1 Changwang School of Honors, Nanjing University of Information Science and Technology, Nanjing, China
2 School of Artificial Intelligence, Nanjing University of Information Science and Technology, Nanjing, China
3 School of Computer and Software, Nanjing University of Information Science and Technology, Nanjing, China
* Corresponding Author:Lina Wang. Email:
Journal of Quantum Computing 2021, 3(3), 107-118. https://doi.org/10.32604/jqc.2021.016857
Received 16 June 2021; Accepted 30 August 2021; Issue published 21 December 2021
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.
Keywords
Cite This Article
L. Wang, Z. Tao, L. Wang and Y. Ren, "A hybrid intrusion detection model based on spatiotemporal features,"
Journal of Quantum Computing, vol. 3, no.3, pp. 107–118, 2021.