Open AccessNetwork intrusion detection algorithm based on deep neural network
Author(s) -
Jia Yang,
Wang Meng,
Wang Yagang
Publication year - 2019
Publication title -
iet information security
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.308
H-Index - 34
eISSN - 1751-8717
pISSN - 1751-8709
DOI - 10.1049/iet-ifs.2018.5258
Subject(s) - intrusion detection system , computer science , data mining , anomaly based intrusion detection system , artificial neural network , intrusion , artificial intelligence , network security , machine learning , algorithm , computer security , geochemistry , geology
With the rapid development of network technology, active defending of the network intrusion is more important than before. In order to improve the intelligence and accuracy of network intrusion detection and reduce false alarms, a new deep neural network (NDNN) model based intrusion detection method is designed. A NDNN with four hidden layers is modelled to capture and classify the intrusion features of the KDD99 and NSL‐KDD training data. Experiments on KDD99 and NSL‐KDD dataset shows that the NDNN‐based method improves the performance of the intrusion detection system (IDS) and the accuracy rate can be obtained as high as 99.9%, which is higher when compared with other dozens of intrusion detection methods. This NDNN model can be applied in IDS to make the system more secure.