AI-Powered Framework for Real-time Threat Detection and Response in Cloud Infrastructure
Zakir Hossain, Md. Emran Hossain, Nur Ahmed, Md Farhad Kabir, Iffat Sania Hossain · Formosa Journal of Multidisciplinary Research · 2025
As most organizations worldwide embrace cloud computing services, cloud infrastructure security has become a significant concern. With cybersecurity attacks changing at an unprecedented rate in the cloud environment, the methods for detection and response must become more robust. This study presents an AI based framework to enhance the real-time detection and response to threats in cloud infrastructure. A possible threat that, if in a real-world scenario, could and would have been detected in real-time and was detected using clustering on e huge amount of cloud traffic. AI algorithms that detect malicious behaviour also assist in calculating the severity of the threat and recommend some flip of a switch to change things instantly. At the heart of the framework is its capacity for cumulative learning about new data, adjusting to emerging attack patterns and achieving low false positive rates. Additionally, it uses a hybrid approach that combines signature based detection with anomaly detection to prevent known and unknown threats. Using this combination, the framework can detect new attack vectors that may be overlooked by traditional means.