A network intrusion detection system based on deep learning in the IoT

Wang, Xiao; Dai, Lie; Yang, Guang

doi:10.1007/s11227-024-06345-w

J Supercomput

2024

DOI: 10.1007/s11227-024-06345-w

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A network intrusion detection system based on deep learning in the IoT

Xiao Wang,

Lie Dai,

Guang Yang

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2024

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Cited by 2 publications

References 68 publications

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The Random Neural Network – based Approach and Evolutionary Intelligence are integral components ofIOT – RNNEI, an intrusion detection system for IOT Networks

RAHMANI,

AREFI,

SHOJAE

et al. 2024

Preprint

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Over the past few years, there has been significant research on the Internet of Things (IOT), with a major challenge being network security and penetration. Security solutions require careful planning and vigilance to safeguard system security and privacy. Adjusting the weights of neural networks has been shown to improve detection accuracy to some extent. In attack detection, the primary goal is to enhance the precision of attack detection using machine learning techniques. The paper details a fresh approach for adjusting weights in the random neural network to recognize attacks. Reviews of the method under consideration indicate better performance than random neural network methods, Nearest Neighbor, and Support Vector Machine (SVM). Up to 99.49% accuracy has been achieved in attack detection, while the random neural network method has improved to 99.01%. The amalgamation of the most effective approaches in these experiments through a multi-learning model led to an accuracy improvement to 99.56%. The proposed model required less training time compared to the random neural network method.

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The Random Neural Network – based Approach and Evolutionary Intelligence are integral components ofIOT – RNNEI, an intrusion detection system for IOT Networks

RAHMANI,

AREFI,

SHOJAE

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

RNNEI: an attack detection model on Internet of Things Networks that utilizes Random Neural Networks and Evolutionary Intelligence

RAHMANI,

Arefi,

Shojae

et al. 2024

Preprint

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A network intrusion detection system based on deep learning in the IoT

Cited by 2 publications

References 68 publications

The Random Neural Network – based Approach and Evolutionary Intelligence are integral components ofIOT – RNNEI, an intrusion detection system for IOT Networks

The Random Neural Network – based Approach and Evolutionary Intelligence are integral components ofIOT – RNNEI, an intrusion detection system for IOT Networks

RNNEI: an attack detection model on Internet of Things Networks that utilizes Random Neural Networks and Evolutionary Intelligence

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