Self Healing Cloud IoT Systems Using Adversarial Machine Learning

  • Authors

    • Salma El-Sayed Department of Biotechnology, Alexandria Life Sciences University, Egypt Author
    • Ahmed Hassan Professor, Cairo University, Egypt Author

    DOI:

    https://doi.org/10.67228/30713315/IJAIDT-2022PI7U6Q

    Published 03-05-2022

  • Self-Healing Systems, Cloud–Iot Architecture, Adversarial Machine Learning, Intrusion Detection, Cyber-Resilience, Reinforcement Learning, Autonomous Recovery, Iot Security, Anomaly Detection, Distributed Systems

    Issue

    Vol. 5 No. 1 (2022)

    Section

    Articles

    How to Cite

    [1]
    S. El-Sayed and A. Hassan, “Self Healing Cloud IoT Systems Using Adversarial Machine Learning”, IJAIDT, vol. 5, no. 1, pp. 01–15, Mar. 2022, doi: 10.67228/30713315/IJAIDT-2022PI7U6Q.

  • Abstract

    The rapid expansion of Cloud–IoT ecosystems has introduced unprecedented scalability and flexibility but also created complex attack surfaces vulnerable to evolving cyber threats. Traditional intrusion detection and fault-tolerance techniques struggle to address adversarial attacks that exploit machine-learning models and disrupt IoT service continuity. This paper proposes a self-healing Cloud–IoT architecture enhanced by adversarial machine learning (AML) to autonomously detect, mitigate, and recover from malicious disruptions. The framework integrates adversarial-resilient anomaly detection, dynamic attack classification, and automated healing modules that leverage reinforcement learning (RL) and predictive models to restore system functionality with minimal human intervention. Experimental evaluations demonstrate improved robustness, reduced downtime, and higher detection accuracy under various adversarial scenarios, proving the effectiveness of AML-driven self-healing mechanisms for next-generation distributed systems. This work highlights essential design considerations and presents future directions for secure, autonomous, and resilient Cloud–IoT infrastructures.

  • References

    [1] Song, Y., Liu, T., Wei, T., Wang, X., Tao, Z., & Chen, M. (2021). FDA³: Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications. IEEE Transactions on Industrial Informatics, 17(11), 7830–7838.

    [2] Aloraini, F., & others (2022). Adversarial Machine Learning in IoT from an Insider Point of View. Journal (or Conference). (Taxonomy and defense methods for insider adversaries in IoT ML systems.)

    [3] Luo, Z., Zhao, S., Lu, Z., Sagduyu, Y. E., & Xu, J. (2020). Adversarial Machine Learning based Partial-model Attack in IoT. arXiv preprint.

    [4] Rosenberg, I., Shabtai, A., Elovici, Y., & Rokach, L. (2021). Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain. ACM Computing Surveys, 54(5), 108. (Comprehensive defense strategies.)

    [5] Li, J., Lyu, L., Liu, X., Zhang, X., & Lyu, X. (2020). FLEAM: A Federated Learning Empowered Architecture to Mitigate DDoS in Industrial IoT. arXiv preprint. (Federated learning + IoT security)

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    [7] Luo, Z., Zhao, S., Lu, Z., Sagduyu, Y. E., Xu, J. (2020). Adversarial Machine Learning Based Partial-Model Attack in IoT.

    [8] Rosenberg, I., Shabtai, A., Elovici, Y., Rokach, L. (2020). Adversarial Machine Learning Attacks and Defense Methods in Cyber Security.

    [9] Selvakkumar, A., Pal, S., Jadidi, Z. (2021). Addressing Adversarial Machine Learning Attacks in Smart Healthcare IoT.

    [10] Hussain, F., Hussain, R., Hassan, S. A., Hossain, E. (2020). Machine Learning in IoT Security: Current Solutions and Future Challenges.

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