An Integrated Framework with Hybrid Anomaly Detection, Predictive Analysis, and Graph-Based Insight for Enhancing Cloud Task Scheduling Security

Abstract

As cloud computing continues to revolutionize the landscape of modern IT infrastructures, ensuring the security of cloud task scheduling has become paramount. This research presents a comprehensive framework that leverages a synergy of cutting-edge technologies to address security risks associated with cloud task scheduling. The proposed framework integrates anomaly detection, predictive analysis, graph-based insights and explainable AI to enhance the task scheduling. The anomaly detection module combines diverse anomaly detection techniques, including statistical methods and deep learning models, to enhance accuracy and minimize false positives and negatives. The integration of graph-based insights capitalizes on the representation of cloud resources and their interactions as a graph. By analyzing structural changes and unusual patterns in this graph, the framework gains a holistic view of the cloud environment, enriching anomaly detection accuracy. To enhance transparency and decision-making, the framework incorporates explainable AI, providing administrators with insights into why specific anomalies are flagged. This fosters informed responses to anomalies and strengthens the human-in-the-loop element of the security strategy. In conclusion, this research advances the field of cloud task scheduling security by introducing an integrated framework that harmonizes anomaly detection, predictive analysis, graph-based insights and AI. By amalgamating these innovations, the framework empowers cloud environments to better safeguard against evolving security threats, ultimately fostering a more secure and resilient cloud ecosystem