2690-6791ISSN (Online)
2769-786XISSN (Print)
Volume 8 , Issue 1 , PP: 55-65, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Lobna Osman 1 * , Olutosin Taiwo 2 , Ahmed Elashry 3 , Absalom E. Ezugwu 4
Doi: https://doi.org/10.54216/JISIoT.080105
Edge computing is a distributed computing paradigm that involves processing data at or near the edge of the internet of things (IoT) network, instead of centralized server. This makes the cyber-attacks increasingly sophisticated, and traditional security measures become no longer sufficient to protect against them. Concurrently, privacy concerns arise when sensitive data is involved in Edge computing applications containing confidential information. In this paper, we propose a privacy-preserved federated learning (FL) approach for cyber-attack detection in edge based IoT ecosystem. A novel lightweight convolutional Transformer network (LCT) network is designed to precisely identify cyber-attacks though learning attack patterns from IoT traffics in local edge devices, where model is personalized though fine-tuning. The privacy of model and data is preserved in our system via incorporating differential privacy and secure aggregation during the cooperative training process on edge devices. We evaluate our proposed approach on a real-world dataset of network traffic (NSL-KDD) containing various types of attacks, and the experimental results show that our personalized FL approach outperforms traditional FL in terms of detection accuracy. We also show that our approach is effective in handling non-stationary data and adapting to changes in the network environment.
Edge Computing , IoT , Intelligent Systems , Data Security , Privacy
[1] Mona Mohamed, A comparative study on Internet of Things (IoT): Frameworks, Tools, Applications and Future directions, Journal of Intelligent Systems and Internet of Things, Vol. 1, No. 1, 13-39, 2020
[2] Q. Luo, S. Hu, C. Li, G. Li, and W. Shi, “Resource Scheduling in Edge Computing: A Survey,” IEEE Commun. Surv. Tutorials, 2021, doi: 10.1109/COMST.2021.3106401.
[3] Ahmed Abdelmonem , Shimaa S. Mohamed, Internet of Things risks, benefits, challenges in industrial application: Survey, American Journal of Business and Operations Research, Vol. 8, No. 1, 47-59, 2022, doi : https://doi.org/10.54216/AJBOR.080105)
[4] X. X. X. Wang et al., “A Survey of Machine and Deep Learning Methods for Internet of Things (IoT) Security,” IEEE Commun. Surv. Tutorials, 2020.
[5] P. Ranaweera, A. D. Jurcut, and M. Liyanage, “Survey on Multi-Access Edge Computing Security and Privacy,” IEEE Communications Surveys and Tutorials. 2021, doi: 10.1109/COMST.2021.3062546.
[6] S. Singh, R. Sulthana, T. Shewale, V. Chamola, A. Benslimane, and B. Sikdar, “Machine-Learning-Assisted Security and Privacy Provisioning for Edge Computing: A Survey,” IEEE Internet Things J., 2022, doi: 10.1109/JIOT.2021.3098051.
[7] K. Sha, T. A. Yang, W. Wei, and S. Davari, “A survey of edge computing-based designs for IoT security,” Digit. Commun. Networks, 2020, doi: 10.1016/j.dcan.2019.08.006.
[8] L. Tawalbeh, F. Muheidat, M. Tawalbeh, and M. Quwaider, “IoT privacy and security: Challenges and solutions,” Appl. Sci., 2020, doi: 10.3390/APP10124102.
[9] A. Alwarafy, K. A. Al-Thelaya, M. Abdallah, J. Schneider, and M. Hamdi, “A Survey on Security and Privacy Issues in Edge-Computing-Assisted Internet of Things,” IEEE Internet of Things Journal. 2021, doi: 10.1109/JIOT.2020.3015432.
[10] L. Nie et al., “Intrusion Detection for Secure Social Internet of Things Based on Collaborative Edge Computing: A Generative Adversarial Network-Based Approach,” IEEE Trans. Comput. Soc. Syst., 2022, doi: 10.1109/TCSS.2021.3063538.
[11] I. Idrissi, M. Azizi, and O. Moussaoui, “A Lightweight Optimized Deep Learning-based Host-Intrusion Detection System Deployed on the Edge for IoT,” Int. J. Comput. Digit. Syst., 2022, doi: 10.12785/ijcds/110117.
[12] Mohammad Hammoudeh , Saeed M. Aljaberi, Modeling of Deep Learning based Intrusion Detection System in Internet of Things Environment, Journal of Cybersecurity and Information Management, Vol. 8, No. 1, 17-25, 2021 doi : https://doi.org/10.54216/JCIM.080102.
[13] S. Nayak, N. Ahmed, and S. Misra, “Deep Learning-Based Reliable Routing Attack Detection Mechanism for Industrial Internet of Things,” Ad Hoc Networks, 2021, doi: 10.1016/j.adhoc.2021.102661.
[14] T. T. Huong et al., “LocKedge: Low-Complexity Cyberattack Detection in IoT Edge Computing,” IEEE Access, 2021, doi: 10.1109/ACCESS.2021.3058528.
[15] T. R. Gadekallu et al., “Blockchain for Edge of Things: Applications, Opportunities, and Challenges,” IEEE Internet Things J., 2022, doi: 10.1109/JIOT.2021.3119639.
[16] Navod Neranjan Thilakarathne , Rohan Samarasinghe , Mohan Krishna Kagita , Surekha Lanka , Hussain Ahmad, Smart Grid: A Survey of Architectural Elements, Machine Learning and Deep Learning Applications and Future Directions, Journal of Intelligent Systems and Internet of Things, Vol. 3, No. 1, 32-42, 2021 doi : https://doi.org/10.54216/JISIoT.030103
[17] P. Kumar, R. Kumar, G. P. Gupta, and R. Tripathi, “BDEdge: Blockchain and Deep-Learning for Secure Edge-Envisioned Green CAVs,” IEEE Trans. Green Commun. Netw., vol. 6, no. 3, pp. 1330–1339, Sep. 2022, doi: 10.1109/TGCN.2022.3165692.
[18] M. Abdel-Basset, N. Moustafa, and H. Hawash, “Privacy-Preserved Cyberattack Detection in Industrial Edge of Things (IEoT): A Blockchain-Orchestrated Federated Learning Approach,” IEEE Trans. Ind. Informatics, vol. 18, no. 11, pp. 7920–7934, Nov. 2022, doi: 10.1109/TII.2022.3167663.
[19] Y. Ye, S. Li, F. Liu, Y. Tang, and W. Hu, “EdgeFed: Optimized Federated Learning Based on Edge Computing,” IEEE Access, 2020, doi: 10.1109/ACCESS.2020.3038287.
[20] L. Dhanabal and S. P. Shantharajah, “A Study on NSL-KDD Dataset for Intrusion Detection System Based on Classification Algorithms,” Int. J. Adv. Res. Comput. Commun. Eng., 2015.
