Multiclass ELM Based Smart Trustworthy IDS for MANETs

Singh, Devendra; Bedi, S. S.

doi:10.1007/s13369-016-2112-8

Cited by 6 publications

(1 citation statement)

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“…In light of the fact that intrusion data often occupies a small part or different attack data distribution, the system uses weighted ELM to perform intrusion detection, thereby improving the accuracy of intrusion detection. 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.…”

Section: Introductionmentioning

confidence: 99%

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

Zheng

Zhou

Wang

et al. 2020

Sensors

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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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Section: Introductionmentioning

confidence: 99%