A Review and Comparative Analysis of Intrusion Detection Systems for Edge Networks in IoT
Keywords:
IoT, Machine Learning, Edge Network, IDS, Deep Learning
Abstract
This article presents a comprehensive review and comparative analysis of intrusion detection systems (IDS) designed for edge networks in Internet of Things (IoT) environments. The rapid growth of IoT has heightened security vulnerabilities in edge networks, which are the focus of this study. Various IDS approaches, including signature-based, anomaly-based, and hybrid methods, are explored, with an emphasis on the application of machine learning and deep learning techniques. The review includes an analysis of system architectures, algorithms (LSTM, CNN, Transformer), datasets, performance evaluation metrics, and experimental results from prior research. The literature study indicates that deep learning has significant potential to enhance intrusion detection accuracy; however, its effectiveness depends on dataset quality, appropriate data preprocessing, and handling class imbalances. Optimal feature selection, blockchain integration, and ensemble approaches are also critical. In conclusion, a multi-faceted approach combining advanced algorithms, suitable preprocessing techniques, and a deep understanding of IoT attacks is essential. Future research should focus on developing adaptive, efficient, and robust IDS with realistic datasets and comprehensive evaluation methods. This article provides a valuable resource for researchers and practitioners in the field of IoT edge network security.
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References
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[2] A. Amrullah, M. U. H. Al Rasyid, and I. Winarno, “Implementasi dan Analisis Protokol Komunikasi IoT untuk Crowdsensing pada Bidang Kesehatan,” INOVTEK Polbeng - Seri Inform., vol. 7, no. 1, p. 122, 2022, doi: 10.35314/isi.v7i1.2365.
[3] S. Bayer, H. Gimpel, and D. Rau, “IoT-commerce - opportunities for customers through an affordance lens,” Electron. Mark., vol. 31, no. 1, pp. 27–50, 2021, doi: 10.1007/s12525-020-00405-8.
[4] M. Satyanarayanan, “The Emergence of Edge Computing,” Computer (Long. Beach. Calif)., vol. 50, no. 1, pp. 30–39, 2017, doi: 10.1109/MC.2017.9.
[5] W. Shi, J. Cao, Q. Zhang, Y. Li, and L. Xu, “Edge Computing: Vision and Challenges,” IEEE Internet Things J., vol. 3, no. 5, pp. 637–646, 2016, doi: 10.1109/JIOT.2016.2579198.
[6] W. Z. Khan, E. Ahmed, S. Hakak, I. Yaqoob, and A. Ahmed, “Edge computing: A survey,” Futur. Gener. Comput. Syst., vol. 97, pp. 219–235, 2019, doi: https://doi.org/10.1016/j.future.2019.02.050.
[7] L. M. Vaquero and L. Rodero-Merino, “Finding your Way in the Fog: Towards a Comprehensive Definition of Fog Computing,” SIGCOMM Comput. Commun. Rev., vol. 44, no. 5, pp. 27–32, Oct. 2014, doi: 10.1145/2677046.2677052.
[8] P. Spadaccino and F. Cuomo, “Intrusion Detection Systems for Iot : Opportunities and Tem Taxonomy Sys-,” 2020, [Online]. Available: https://arxiv.org/abs/2012.01174
[9] Z. Lin, Y. Shi, and Z. Xue, “IDSGAN: Generative Adversarial Networks for Attack Generation Against Intrusion Detection,” in Advances in Knowledge Discovery and Data Mining, J. Gama, T. Li, Y. Yu, E. Chen, Y. Zheng, and F. Teng, Eds., Cham: Springer International Publishing, 2022, pp. 79–91.
[10] M. M. Rahman, S. Al Shakil, and M. R. Mustakim, “A survey on intrusion detection system in IoT networks,” Cyber Secur. Appl., vol. 3, p. 100082, 2025, doi: https://doi.org/10.1016/j.csa.2024.100082.
[11] Y. Liu, Z. Pang, M. Karlsson, and S. Gong, “Anomaly detection based on machine learning in IoT-based vertical plant wall for indoor climate control,” Build. Environ., vol. 183, p. 107212, 2020, doi: https://doi.org/10.1016/j.buildenv.2020.107212.
[12] C. Alex, G. Creado, W. Almobaideen, O. A. Alghanam, and M. Saadeh, “A Comprehensive Survey for IoT Security Datasets Taxonomy, Classification and Machine Learning Mechanisms,” Comput. Secur., vol. 132, p. 103283, 2023, doi: https://doi.org/10.1016/j.cose.2023.103283.
[13] B. Kaur et al., “Internet of Things (IoT) security dataset evolution: Challenges and future directions,” Internet of Things, vol. 22, p. 100780, 2023, doi: https://doi.org/10.1016/j.iot.2023.100780.
[14] N. Mazhar, R. Salleh, M. A. Hossain, and M. Zeeshan, “Paper_83-SDN_based_Intrusion_Detection_and_Prevention.pdf,” vol. 11, no. 12, 2020.
[15] A. Dorri, S. S. Kanhere, R. Jurdak, and P. Gauravaram, “Blockchain for IoT security and privacy: The case study of a smart home,” in 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 2017, pp. 618–623. doi: 10.1109/PERCOMW.2017.7917634.
[16] P. Sun, Y. Wan, Z. Wu, Z. Fang, and Q. Li, “A survey on privacy and security issues in IoT-based environments: Technologies, protection measures and future directions,” Comput. Secur., vol. 148, p. 104097, 2025, doi: https://doi.org/10.1016/j.cose.2024.104097.
[17] S. El-Gendy, M. S. Elsayed, A. Jurcut, and M. A. Azer, “Privacy Preservation Using Machine Learning in the Internet of Things,” Mathematics, vol. 11, no. 16, 2023, doi: 10.3390/math11163477.
[18] G. Akar, S. Sahmoud, and M. O. Member, “L2D2 : A Novel LSTM Model for Multi-class Intrusion Detection Systems in The Era of IoMT,” vol. 11, pp. 1–12, 2025, doi: 10.1109/ACCESS.2025.3526883.
[19] M. E. Manaa, S. M. Hussain, S. A. Alasadi, and H. A.a.al-Khamees, “DDoS Attacks Detection based on Machine Learning Algorithms in IoT Environments,” Intel. Artif., vol. 27, no. 74, pp. 152–165, 2024, doi: 10.4114/intartif.vol27iss74pp152-165.
[20] H. Kamal, “Advanced Hybrid Transformer-CNN Deep Learning Model for Effective Intrusion Detection Systems with Class Imbalance Mitigation Using Resampling Techniques,” Futur. Internet, vol. 16, no. 12, 2024, doi: 10.3390/fi16120481.
[21] O. P. Olawale and S. Ebadinezhad, “Cybersecurity Anomaly Detection: AI and Ethereum Blockchain for a Secure and Tamperproof IoHT Data Management,” IEEE Access, vol. 12, no. August, pp. 131605–131620, 2024, doi: 10.1109/ACCESS.2024.3460428.
[22] B. Al Sukhni, S. K. Manna, J. M. Dave, and L. Zhang, “Extracting Optimal Number of Features for Machine Learning Models in Multilayer IoT Attacks,” Sensors, vol. 24, no. 24, 2024, doi: 10.3390/s24248121.
[23] M. Esmaeili, M. Rahimi, H. Pishdast, D. Farahmandazad, and M. Khajavi, “Machine Learning-Assisted Intrusion Detection for Enhancing Internet of Things Security,” pp. 1–34, 2024.
[24] D. Rani, N. S. Gill, P. Gulia, F. Arena, and G. Pau, “Design of an Intrusion Detection Model for IoT-Enabled Smart Home,” IEEE Access, vol. 11, no. April, pp. 52509–52526, 2023, doi: 10.1109/ACCESS.2023.3276863.
Published
2025-01-24
How to Cite
[1]
A. Amrullah, “A Review and Comparative Analysis of Intrusion Detection Systems for Edge Networks in IoT”, Intellithings Journal, vol. 1, no. 1, pp. 1-10, Jan. 2025.
Section
Articles
