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.

A review on extreme learning machine

“…The experimental results proved this method was efficient and rapid. In 2020, Zheng and Hong [238] put forward a novel approach for intrusion detection which combined several useful scheme including extreme learning machine. They first applied improved linear discriminant analysis (LDA) for reduction of feature dimensions.…”

A Novel Attack Detection Scheme for the Industrial Internet of Things Using a Lightweight Random Neural Network

“…For phishing and botnet attacks, their experimental results provided accuracy values of 94.30% and 94.80% respectively. Zheng et al [22] proposed a linear discriminant analysis-based extreme learning technique for IoT intrusion detection. The researchers evaluated the accuracy of the proposed scheme by utilizing the NSL-KDD dataset.…”

Network Intrusion Detection Based on an Improved Long‐Short‐Term Memory Model in Combination with Multiple Spatiotemporal Structures

“…Recently, most of the standard examples used in the field of intrusion detection are still the KDDCUP99 dataset, and the test results in KDDCUP99 are better under certain conditions [32]. However, the KDDCUP99 dataset simulated 20 years ago is no longer suitable for the modern intelligent and complex attack methods, such as penetration utilization, SQL injection, APT, and complex hidden attack forms.…”