Computation Intelligence Techniques for Security in IoT Devices
DOI:
https://doi.org/10.63503/j.ijcma.2025.48Keywords:
Internet of Things, Fifth Generation (5G), Computational Intelligence Techniques (CIT), Security, Machine Learning, Deep Learning, Fuzzy SystemsAbstract
This paper provides an overview of the context of security in Internet of Things (IoT) devices. It introduces the fundamentals of IoT definitions, and fifth genera tion (5G) networks and focuses on security in IoT devices. An introduction to computational intelligence techniques is also presented, including their evolution, use cases, significance, and standardization efforts, with examples. This paper presents a taxonomy of cyber threats targeting IoT devices and a review of sever al key works in every security category in IoT devices. It also explores the applica tion of computational intelligence techniques to enhance the security of IoT de vices. Providing a comprehensive overview of models or mechanisms such as machine learning (ML), deep learning (DL), fuzzy systems (FS), and evolutionary algorithms (EA). Their model role is to detect and mitigate security threats in the IoT system. The paper shows the successful application of computational intelli gence in enhancing IoT security through case studies and practical examples. Next, this paper discusses new challenges and future research directions in IoT device security.
References
[1]. J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, “Internet of Things (IoT): A vision, architectural elements, and future directions,” Future Generation Computer Systems, Vol. 29, No. 7, pp. 1645–1660, 2013.
[2]. J. G. Andrews, S. Buzzi, W. Choi, S. V. Hanly, A. Lozano, A. C. Soong, and J. C. Zhang, “What will 5G be?” IEEE Journal on Selected Areas in Communications, Vol. 32, No. 6, pp. 1065–1082, 2014.
[3]. L. A. Zadeh, “Fuzzy sets,” Information and Control, Vol. 8, No. 3, pp. 338–353, 1965.
[4]. S. Li, L. D. Xu, and S. Zhao, “The Internet of Things: a survey,” Information Systems Frontiers, Vol. 17, pp. 243–259, 2015.
[5]. R. Roman, P. Najera, and J. Lopez, “Securing the Internet of Things,” Computer, Vol. 44, No. 9, pp. 51–58, 2011.
[6]. ISO/IEC 30141:2018, Internet of Things (IoT) – Reference Architecture, International Organization for Standardization, 2018.
[7]. C. Kolias, G. Kambourakis, A. Stavrou, and J. Voas, “DDoS in the IoT: Mirai and other botnets,” Computer, Vol. 50, No. 7, pp. 80–84, 2017.
[8]. A. Mosenia and N. K. Jha, “A comprehensive study of security of Internet-of-Things,” IEEE Transactions on Emerging Topics in Computing, Vol. 5, No. 4, pp. 586–602, 2017.
[9]. M. S. Hossain, M. Fotouhi, and R. Hasan, “Towards an analysis of security issues, challenges, and open problems in the Internet of Things,” Proceedings of the IEEE World Congress on Services, 2015, pp. 21–28.
[10]. J. Granjal, E. Monteiro, and J. Sa Silva, “Security for the Internet of Things: A survey of existing protocols and open research issues,” IEEE Communications Surveys & Tutorials, Vol. 17, No. 3, pp. 1294–1312, 2015.
[11]. D. A. Fernandes, L. F. Soares, J. V. Gomes, M. M. Freire, and P. R. Inácio, “Security issues in cloud environments: a survey,” International Journal of Information Security, Vol. 13, No. 2, pp. 113–170, 2014.
[12]. R. Doshi, N. Apthorpe, and N. Feamster, “Machine learning DDoS detection for consumer Internet of Things devices,” 2018 IEEE Security and Privacy Workshops (SPW), 2018, pp. 29–35.
[13]. L. Deng and D. Yu, Deep learning: Methods and applications, Foundations and Trends in Signal Processing, Vol. 7, No. 3–4, pp. 197–387, 2014.
[14]. L. A. Zadeh, “Fuzzy sets,” Information and Control, Vol. 8, No. 3, pp. 338–353, 1965.
[15]. D. E. Goldberg and J. H. Holland, “Genetic algorithms and machine learning,” Machine Learning, Vol. 3, No. 2, pp. 95–99, 1988.
[16]. N. Apthorpe, D. Reisman, and N. Feamster, “Closing the blinds: Four strategies for protecting smart home privacy from network observers,” arXiv preprint arXiv:1705.06809, 2017.
[17]. J. Gubbi, R. Buyya, S. Marusic, and M. Palaniswami, “Internet of Things (IoT): A vision, architectural elements, and future directions,” Future Generation Computer Systems, Vol. 29, No. 7, pp. 1645–1660, 2013.
[18]. D. M. Farid and C. M. M. Rahman, “Anomaly network intrusion detection based on improved self-adaptive Bayesian algorithm,” Journal of Computers, Vol. 5, No. 1, pp. 23–31, 2010.
[19]. J. Biamonte, P. Wittek, N. Pancotti, P. Rebentrost, N. Wiebe, and S. Lloyd, “Quantum machine learning,” Nature, Vol. 549, No. 7671, pp. 195–202, 2017.
[20]. G. Shrivastava, S.L. Peng, H. Bansal, K. Sharma, M. Sharma, eds. New age analytics: Transforming the internet through machine learning, IoT, and trust modeling. CRC Press, 2020.
[21]. M. Khari, G. Shrivastava, S. Gupta, R. Gupta. "Role of cyber security in today's scenario." In Detecting and mitigating robotic cyber security risks, IGI Global, pp. 177-191, 2017.
[22]. H. Sharma, P. Kumar, K. Sharma. "Recurrent Neural Network based Incremental model for Intrusion Detection System in IoT." Scalable Computing: Practice and Experience, Vol. 25, no. 5, pp. 3778-3795, 2024.