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Open AccessArticle

An Improved LDA-Based ELM Classification for Intrusion Detection Algorithm in IoT Application

by 1, 2,3,*, 4,* and 5,*
1
Faculty of Mechanical Engineering & Automation, Zhejiang Sci-Tech University, Hangzhou 310018, China
2
College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
3
School of Electrical & Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA
4
School of Public Administration, Zhejiang University of Finance & Economics, Hangzhou 310018, China
5
Zhejiang Provincial Testing Institute of Electronic Information Products, Hangzhou 310007, China
*
Authors to whom correspondence should be addressed.
Sensors 2020, 20(6), 1706; https://doi.org/10.3390/s20061706
Received: 23 January 2020 / Revised: 23 February 2020 / Accepted: 13 March 2020 / Published: 19 March 2020
(This article belongs to the Section Internet of Things)
The Internet of Things (IoT) is widely applied in modern human life, e.g., smart home and intelligent transportation. However, it is vulnerable to malicious attacks, and the current existing security mechanisms cannot completely protect the IoT. As a security technology, intrusion detection can defend IoT devices from most malicious attacks. However, unfortunately the traditional intrusion detection models have defects in terms of time efficiency and detection efficiency. Therefore, in this paper, we propose an improved linear discriminant analysis (LDA)-based extreme learning machine (ELM) classification for the intrusion detection algorithm (ILECA). First, we improve the linear discriminant analysis (LDA) and then use it to reduce the feature dimensions. Moreover, we use a single hidden layer neural network extreme learning machine (ELM) algorithm to classify the dimensionality-reduced data. Considering the high requirement of IoT devices for detection efficiency, our scheme not only ensures the accuracy of intrusion detection, but also improves the execution efficiency, which can quickly identify the intrusion. Finally, we conduct experiments on the NSL-KDD dataset. The evaluation results show that the proposed ILECA has good generalization and real-time characteristics, and the detection accuracy is up to 92.35%, which is better than other typical algorithms. View Full-Text
Keywords: IoT; intrusion detection; linear discriminant analysis; extreme learning machine; classification IoT; intrusion detection; linear discriminant analysis; extreme learning machine; classification
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Zheng, D.; Hong, Z.; Wang, N.; Chen, P. An Improved LDA-Based ELM Classification for Intrusion Detection Algorithm in IoT Application. Sensors 2020, 20, 1706.

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