• Jeyasri Sekar

The rapid proliferation of Internet of Things (IoT) devices has revolutionized various sectors by enabling seamless connectivity and data exchange. However, the massive influx of data and the heightened risk of security breaches necessitate advanced solutions. This research explores the integration of Artificial Intelligence (AI) into cloud-based IoT systems, proposing a multi-layered approach to enhance data analytics and security. We leverage AI-driven techniques to process and analyze vast amounts of data in real time, providing actionable insights while fortifying system security through intelligent threat detection and mitigation strategies. Our multi-layered framework incorporates advanced machine learning algorithms for predictive analytics, anomaly detection, and dynamic security policies, creating a robust and resilient IoT ecosystem. The results demonstrate significant improvements in data processing efficiency, predictive accuracy, and overall system security. This study underscores the potential of AI to transform cloud-based IoT systems, offering a scalable and secure solution to meet the evolving demands of modern technology landscapes.

Cloud-Based IoT Systems, Artificial Intelligence, Data Analytics, Security, Multi-Layered Approach, Machine Learning, Predictive Analytics

"ENHANCING CLOUD-BASED IOT SYSTEMS WITH AI: A MULTI-LAYERED APPROACH TO DATA ANALYTICS AND SECURITY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 5, page no.p581-p589, May-2023, Available :http://www.jetir.org/papers/JETIR2305G79.pdf

"ENHANCING CLOUD-BASED IOT SYSTEMS WITH AI: A MULTI-LAYERED APPROACH TO DATA ANALYTICS AND SECURITY", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 5, page no. ppp581-p589, May-2023, Available at : http://www.jetir.org/papers/JETIR2305G79.pdf