Detecting attacks in Internet of Things infrastructure using improved chimpanzee algorithm and deep learning.

Articles in Press

Document Type : Original Article

Authors

1 PhD student, Department of Computer Engineering, Faculty of Computer Science, Islamic Azad University, Urmia, Iran

2 Assistant Professor, Department of Computer Engineering, Faculty of Computer Science, Islamic Azad University, Urmia, Iran

3 Associate Professor, Department of Computer Engineering, Faculty of Computer Science, Islamic Azad University, Urmia, Iran

Abstract

The increase in the number of Internet of Things devices, high speed, and large volume of generated data has led to network security issues and the identification of cyber attacks in these networks becoming one of the key challenges in this area. Intrusion detection systems have been proposed as a solution to tackle this problem. Proper selection of features in creating intrusion detection models can significantly increase detection accuracy. In this article, a binary algorithm and an improved chimpanzee algorithm have been designed for feature selection. The chimpanzee algorithm is designed for solving continuous problems and cannot be efficient in solving binary problems. It also suffers from local optima and slow exploration, exploitation, and convergence in this algorithm. Therefore, changes need to be made in this algorithm to solve binary problems. Hence، in this article an improved version of the chimpanzee algorithm for discrete problems and feature selection has been designed and implemented for intrusion detection and network-based attacks in Internet of Things networks. The proposed method reduces features by an average of 60 percent and successfully detects attacks with ac curacies of 99.3%, 99.6%, and 99.9% in the Ton-IoT، UNSW-NB15، and IoTID20 datasets, significantly reducing the false alarm rate of detected attacks. Statistical analysis using the Kruskal-Wallis test showed that the proposed method converges faster compared to the comparison methods.

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Electronic and Cyber Defense

Articles in Press, Accepted Manuscript
Available Online from 08 July 2025

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History

  • Receive Date: 29 March 2025
  • Revise Date: 08 June 2025
  • Accept Date: 27 June 2025
  • Publish Date: 08 July 2025

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