Journal
SENSORS
Volume 20, Issue 6, Pages -Publisher
MDPI
DOI: 10.3390/s20061706
Keywords
IoT; intrusion detection; linear discriminant analysis; extreme learning machine; classification
Funding
- Zhejiang Provincial Natural Science Foundation of China [LY20F020030]
- National Natural Science Foundation of China [51708487]
- Zhejiang Provincial public technology application research project [LGF18F010008]
- New Century 151 Talent Project of Zhejiang Province
- Zhejiang Provincial Key Laboratory of Information Security Opening Fund
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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.
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