@Article{jqc.2021.025373,
AUTHOR = {Taifeng Pan},
TITLE = {Intrusion Detection Method of Internet of Things Based on Multi GBDT Feature Dimensionality Reduction and Hierarchical Traffic Detection},
JOURNAL = {Journal of Quantum Computing},
VOLUME = {3},
YEAR = {2021},
NUMBER = {4},
PAGES = {161--171},
URL = {http://www.techscience.com/jqc/v3n4/46226},
ISSN = {2579-0145},
ABSTRACT = {The rapid development of Internet of Things (IoT) technology has 
brought great convenience to people’s life. However, the security protection 
capability of IoT is weak and vulnerable. Therefore, more protection needs to be 
done for the security of IoT. The paper proposes an intrusion detection method for 
IoT based on multi GBDT feature reduction and hierarchical traffic detection 
model. Firstly, GBDT is used to filter the features of IoT traffic data sets BoT-IoT 
and UNSW-NB15 to reduce the traffic feature dimension. At the same time, in 
order to improve the reliability of feature filtering, this paper constructs multiple 
GBDT models to filter the features of multiple sub data sets, and comprehensively 
evaluates the filtered features to find out the best alternative features. Then, two 
neural networks are trained with the two data sets after dimensionality reduction, 
and the traffic will be detected with the trained neural network. In order to improve 
the efficiency of traffic detection, this paper proposes a hierarchical traffic 
detection model, which can reduce the computational cost and time cost of 
detection process. Experiments show that the multi GBDT dimensionality 
reduction method can obtain better features than the traditional PCA 
dimensionality reduction method. Besides, the use of dual data sets improves the 
comprehensiveness of the IoT intrusion detection system, which can detect more 
types of attacks, and the hierarchical traffic model improves the detection 
efficiency of the system.},
DOI = {10.32604/jqc.2021.025373}
}