@Article{cmes.2021.016866,
AUTHOR = {Yanyan Zhang, Xiangjin Ran},
TITLE = {A Step-Based Deep Learning Approach for Network Intrusion Detection},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {128},
YEAR = {2021},
NUMBER = {3},
PAGES = {1231--1245},
URL = {http://www.techscience.com/CMES/v128n3/44020},
ISSN = {1526-1506},
ABSTRACT = {In the network security field, the network intrusion detection system (NIDS) is considered one of the critical issues
in the detection accuracy and missed detection rate. In this paper, a method of two-step network intrusion detection
on the basis of GoogLeNet Inception and deep convolutional neural networks (CNNs) models is proposed.
The proposed method used the GoogLeNet Inception model to identify the network packets’ binary problem.
Subsequently, the characteristics of the packets’ raw data and the traffic features are extracted. The CNNs model
is also used to identify the multiclass intrusions by the network packets’ features. In the experimental results, the
proposed method shows an improvement in the identification accuracy, where it achieves up to 99.63%. In addition,
the missed detection rate is reduced to be 0.1%. The results prove the high performance of the proposed method
in enhancing the NIDS’s reliability.},
DOI = {10.32604/cmes.2021.016866}
}