Paper Info
Title
An Improved LDA-Based ELM Classification for Intrusion Detection Algorithm in IoT Application.
Abstract
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.
Year
DOI
Venue
2020
10.3390/s20061706
SENSORS
Keywords
DocType
Volume
IoT,intrusion detection,linear discriminant analysis,extreme learning machine,classification
Journal
20
Issue
ISSN
Citations 
6
1424-8220
0
PageRank 
References 
Authors
0.34
0
4
Name
Order
Citations
PageRank
Dehua Zheng100.34
Zhen Hong200.34
Ning Wang323087.46
Ping Chen400.34