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A Hybrid Classification Approach for Intrusion Detection in IoT Network

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Title A Hybrid Classification Approach for Intrusion Detection in IoT Network
 
Creator Choudhary, S
Kesswani, N
 
Subject Internet of Things (IoT)
Intrusion Detection Systems (IDS)
Linear Discriminant Analysis (LDA)
PCA
SVM
 
Description 809-816
With the increase in number of IoT devices, the capabilities to provide reliable security and detect the malicious activities within the IoT network have become quite challenging. We propose a hybrid classification approach to detect multi-class attacks in the IoT network. In the proposed model, Principle Component Analysis (PCA) is used to extract the useful features and Linear Discriminant Analysis (LDA) is used to reduce the high dimension data set into lower dimension space by keeping less number of important features. This was assisted by use of a combination of neural network and Support Vector Machine (SVM) classifiers to improve the detection rate and decrease the false alarm rate. The neural network, a multi-class classifier, is used to classify the intruders in the network with more accuracy. The SVM is an efficient and fast learner classifier which is used to classify the unmatched behavior. The proposed method needs less computation complexity for intrusion detection. The performance of the proposed model was evaluated on two benchmark datasets for intrusion detection, i.e., NSL-KDD and UNSW-NB15. Results show that our model outperforms existing models.
 
Date 2021-09-24T05:17:58Z
2021-09-24T05:17:58Z
2021-09
 
Type Article
 
Identifier 0975-1084 (Online); 0022-4456 (Print)
http://nopr.niscair.res.in/handle/123456789/58137
 
Language en
 
Publisher NIScPR-CSIR, India
 
Source JSIR Vol.80(09) [September 2021]