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.
Highlights
The Internet of Things (IoT) is an extension of the traditional network that combines various sensing devices with the Internet
To address the above problems, we propose an improved linear discriminant analysis (LDA)-based extreme learning machine (ELM) classification for the intrusion detection algorithm that achieves rapid and efficient detection of attacks
To ensure the security of the IoT and realize the rapid and efficient network intrusion detection, we mainly focus on the detection algorithms of the intrusion detection module
Summary
The Internet of Things (IoT) is an extension of the traditional network that combines various sensing devices with the Internet. Singh et al [23] proposed the IDS by combining three stages: feature selection, trust calculation, and classification decision This method comprehensively examines the security of the nodes, so as to improve the accuracy of the network’s intrusion detection. As a result of the expansion of the scale of IoT devices, the collected data presents multiple features, complex structures, and high feature dimensions This makes the existing intrusion detection algorithms degradation of real-time and detection performance for such high-dimensional feature data. For IoT intrusion detection, we use the similarity measure function of high-dimensional data space as the weight to improve the between-class scatter matrix, and combine it with LDA to obtain the optimal transformation matrix to achieve the dimensionality reduction of the data.
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