Industrial IoT Monitoring and Threat Mitigation with Federated AI in Real-Time
Keywords:
Federated AI, Industrial IoT, real-time monitoring, federated learningAbstract
Industrial Internet of things systems are exposed to number of cyber security attacks because of its interconnectivity and integration to smart devices in critical infrastructure. IIoT real time monitoring and threat prevention is difficult to scale due to privacy and data sharing concerns. To overcome this problem Federated AI provides a way by using scattered learning across IoT devices without data aggregation which enhances IIoT real-time monitoring and threat detections by securing data privacy, minimising latency, and identifying the threats.
Downloads
References
Xu, X., & Li, Y. (2020). Federated learning for collaborative threat mitigation in industrial IoT systems. IEEE Transactions on Industrial Informatics, 16(4), 2495–2505. https://doi.org/10.1109/TII.2020.3003201
Sivaraman, Hariprasad. "Intelligent Code Coverage Optimization Using Machine Learning for Large Scale Systems." International Journal for Multidisciplinary Research 5.5 (2023).
Pillai, V. “Data Analytics and Engineering in Automobile Data Systems”. Journal of Science & Technology, vol. 4, no. 6, Dec. 2023, pp. 140-79, https://thesciencebrigade.com/jst/article/view/520
S. Kumari, “Cybersecurity Risk Mitigation in Agile Digital Transformation: Leveraging AI for Real-Time Vulnerability Scanning and Incident Response ”, Adv. in Deep Learning Techniques, vol. 3, no. 2, pp. 50–74, Dec. 2023
Singu, Santosh Kumar. "Migration strategies for legacy data warehousing systems to cloud platforms." Internafional Journal of Science and Research (IJSR) 12, no. 12 (2023): 2164-2167.
Tamanampudi, Venkata Mohit. "AI Agents in DevOps: Implementing Autonomous Agents for Self-Healing Systems and Automated Deployment in Cloud Environments." Australian Journal of Machine Learning Research & Applications 3.1 (2023): 507-556.
Machireddy, Jeshwanth Reddy, and Harini Devapatla. "Enhancing Predictive Analytics with AI-Powered RPA in Cloud Data Warehousing: A Comparative Study of Traditional and Modern Approaches." Journal of Deep Learning in Genomic Data Analysis 3.1 (2023): 74-99.
Zhu, Yue, and Johnathan Crowell. "Systematic Review of Advancing Machine Learning Through Cross-Domain Analysis of Unlabeled Data." Journal of Science & Technology 4.1 (2023): 136-155.
Pillai, Vinayak. “Implementing Efficient Data Operations: An Innovative Approach”. Asian Journal of Multidisciplinary Research & Review, vol. 3, no. 6, Dec. 2022, pp. 231-67, https://ajmrr.org/journal/article/view/241.
S. Kumari, “Leveraging AI for Cybersecurity in Agile Cloud-Based Platforms: Real-Time Anomaly Detection and Threat Mitigation in DevOps Pipelines”, J. of Artificial Int. Research and App., vol. 3, no. 1, pp. 698–715, May 2023
Alam, Khorshed, et al. "Designing Autonomous Carbon Reduction Mechanisms: A Data-Driven Approach in Renewable Energy Systems." Well Testing Journal 32.2 (2023): 103-129.
Sivaraman, Hariprasad. (2023). A Machine Learning Paradigm for Cross-Sector Financial Crime Prevention. 14.
Ravichandran, Prabu, Jeshwanth Reddy Machireddy, and Sareen Kumar Rachakatla. "Data Analytics Automation with AI: A Comparative Study of Traditional and Generative AI Approaches." Journal of Bioinformatics and Artificial Intelligence 3.2 (2023): 168-190.
V. Pillai, “Anomaly Detection in Financial and Insurance Data-Systems”, Journal of AI-Assisted Scientific Discovery, vol. 4, no. 2, pp. 144–183, Sep. 2024
Sangaraju, Varun Varma, and Kathleen Hargiss. "Zero trust security and multifactor authentication in fog computing environment." Available at SSRN 4472055.
S. Kumari, “AI-Driven Product Management Strategies for Enhancing Customer-Centric Mobile Product Development: Leveraging Machine Learning for Feature Prioritization and User Experience Optimization ”, Cybersecurity & Net. Def. Research, vol. 3, no. 2, pp. 218–236, Nov. 2023.
Al Imran, Md, Abdullah Al Fathah, Abdullah Al Baki, Khorshed Alam, Md Ali Mostakim, Upal Mahmud, and M. S. Hossen. "Integrating IoT and AI For Predictive Maintenance in Smart Power Grid Systems to Minimize Energy Loss and Carbon Footprint." Journal of Applied Optics 44, no. 1 (2023): 27-47.
Sivaraman, Hariprasad. (2021). INTELLIGENT AUTOMATION FOR SERVICE DEGRADATION PREDICTION USING LLMS AND OBSERVABILITY DATA. International Journal of Engineering Management. 6. 10.5281/zenodo.14342920.
S. Kumari, “AI-Powered Agile Project Management for Mobile Product Development: Enhancing Time-to-Market and Feature Delivery Through Machine Learning and Predictive Analytics”, African J. of Artificial Int. and Sust. Dev., vol. 3, no. 2, pp. 342–360, Dec. 2023
Alam, K., M. A. Mostakim, A. A. Baki, and M. S. Hossen. "CURRENT TRENDS IN PHOTOVOLTAIC THERMAL (PVT) SYSTEMS: A REVIEW OF TECHNOLOGIES AND SUSTAINABLE ENERGY SOLUTIONS." Academic Journal on Business Administration, Innovation & Sustainability 4, no. 04 (2024): 128-143.
Sharma, R., & Patel, J. (2020). Secure and privacy-preserving federated learning in industrial IoT systems. Computers & Security, 98, 102012. https://doi.org/10.1016/j.cose.2020.102012
Wang, T., & Zhang, Z. (2021). Adaptive federated AI for real-time threat detection and mitigation in industrial IoT systems. International Journal of Computer Applications in Technology, 67(2), 145–160. https://doi.org/10.1504/IJCAT.2021.10029234
Singh, A., & Kumar, R. (2022). Enhancing IoT security with federated AI: Challenges and opportunities. IEEE Access, 10, 5312–5325. https://doi.org/10.1109/ACCESS.2022.3167741
Sharma, M., & Gupta, S. (2021). Privacy-preserving federated learning for IoT security. Cybersecurity and Privacy, 1(2), 94–110. https://doi.org/10.3390/cp1020007
Choi, S., & Lee, J. (2020). Real-time IoT security using federated learning: A survey. Journal of Internet Technology, 21(5), 1155–1169. https://doi.org/10.6138/JIT.2020.21.5.1155
Gupta, N., & Kapoor, A. (2021). Federated learning for IoT cybersecurity: A comprehensive review. Journal of Network and Computer Applications, 156, 102573. https://doi.org/10.1016/j.jnca.2020.102573
