Smart Home Intrusion Detection Model based on Principal Component Analysis and Random Forest Classification

Document Type : Original Article

Author

Assistant Professor, Golestan University, Gorgan, Iran

Abstract

In recent years, the issue of maintaining the security of smart homes, where a large number of devices use Internet connections to communicate, has become one of the main concerns in the field of network security. Although a lot of research has been done to establish the security of smart homes, but considering the scope of the topic under discussion, most of these works do not have the necessary efficiency in terms of accuracy and speed of operation. In the proposed method, after performing some pre-processing operations on the dataset, with the help of Principal Component Analysis (PCA), a subset of the features of the dataset are selected to prepare the data for classification, which are the most effective features in intrusion detection. It is considered that this action leads to an increase in the accuracy and speed of the classification action. Also, in the classification stage, the random forest algorithm, which is a powerful algorithm based on machine learning, has been used on a very new dataset of the Internet of Things, called IoTID20. The proposed approach has shown high performance for intrusion detection with an accuracy of 99.73% and 98.46% for the classification of binary and multi-class attacks. Comparing the results of the proposed method with other works, it shows the superiority of the proposed method in detecting multi-class attacks.

Keywords

Main Subjects

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References

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Electronic and Cyber Defense
Volume 12, Issue 2 - Serial Number 46
number 46, summer 2024
September 2024

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History

  • Receive Date: 04 June 2024
  • Revise Date: 27 July 2024
  • Accept Date: 05 August 2024
  • Publish Date: 31 August 2024

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