AI-Powered Framework for Real-time Threat Detection and Response in Cloud Infrastructure

Zakir Hossain; Md Emran Hossain; Nisher Ahmed; Md Farhad Kabir; Iffat Sania Hossain

doi:10.55927/fjmr.v4i4.168

Authors

Zakir Hossain California State University, Northridge
Md Emran Hossain Westcliff University
Nisher Ahmed Westcliff University
Md Farhad Kabir University of Southern California
Iffat Sania Hossain California State University

DOI:

https://doi.org/10.55927/fjmr.v4i4.168

Keywords:

AI-Powered Framework, Real-Time Threat Detection, Cloud Infrastructure, Machine Learning, Anomaly Detection

Abstract

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.

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