Open Access

ARTICLE

A Hybrid Intrusion Detection Model Based on Spatiotemporal Features

by

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.

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