Review on Hybrid Secure AI-IDS Real-Time ML Detection with Container-Serverless Cloud  Protection

Author(s)	Sayyed Aiman Sadique, Dr. Supriya S. Sawwashere, Dr. S. V. Sonekar, Dr. Ashutosh Lanjewar, Dr. Mirza Moiz Baig
Country	India
Abstract	The exponential growth of cyber threats targeting cloud-based systems necessitates intelligent, adaptive,and scalable intrusion detection mechanisms. Traditional Intrusion Detection Systems (IDS) often fail to detect sophisticated or zero-day attacks due to static rules and limited adaptability. This paper proposes HybridSecure AI-IDS, an advanced real-time intrusion detection framework that integrates Machine Learning (ML) with containerized and serverless cloud architectures. The system combines signature- based and anomaly-based models to ensure comprehensive threat coverage. By leveraging container orchestration (Kubernetes) and serverless computing (AWS Lambda, Azure Functions), the framework ensures auto-scaling, resilience, and minimal latency. Experimental results demonstrate improved detection accuracy, low false alarm rate, and enhanced performance in distributed environments compared to traditional IDS systems.
Keywords	Intrusion Detection System (IDS), Machine Learning, Cloud Security, Serverless Computing, Containers, Real-Time Detection, Hybrid AI-IDS.
Published In	Volume 7, Issue 2, March-April 2026
Published On	2026-04-24
DOI	https://doi.org/10.63363/aijfr.2026.v07i02.5174

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