Self-Evolving AI Agent Framework for Predicting and Preventing Zero-Day Cyberattacks in a Multi-Cloud Environment Using Federated Learning and Explainable AI

Abstract: The proposed framework uses intelligent AI agents to continuously monitor cloud activities, detect unusual patterns, and identify potential security threats at an early stage. Federated learning enables different cloud environments to work together in training AI models without sharing their raw security data, thereby helping to maintain data privacy. Explainable AI makes the model’s decisions easier to understand by providing clear reasons behind its predictions, allowing security professionals to investigate suspicious activities and take appropriate action. Additionally, the self- evolving component is designed to adapt to emerging threats by learning from new information and changing attack patterns.

Overall, the framework aims to strengthen threat detection, protect sensitive information, and improve transparency in multi-cloud environments. However, its actual performance needs to be verified through practical implementation and testing with realistic datasets and simulated cyberattacks. Future research should focus on evaluating important factors such as detection accuracy, false-positive rate, response time, and communication overhead to determine the framework’s effectiveness and suitability for real-world cloud security applications.

Keywords: Zero-day cyberattacks, artificial intelligence, self-evolving AI agents, multi-cloud security, federated learning.