ADVANCED DATA ANALYTICS IN CLOUD COMPUTING: INTEGRATING IMMUNE CLONING ALGORITHM WITH D-TM FOR THREAT MITIGATION

Abstract

Cloud Computing is flexible and cost-effective, there are security vulnerabilities because of its centralized structure. In order to improve cloud security, this paper suggests a hybrid architecture that combines data-driven threat mitigation (d-TM) with immune cloning methods. The immune cloning algorithm, which is based on biological immune systems, quickly identifies abnormalities and eliminates risks. The system's integration with d-TM enhances threat detection precision, minimizes false positives, and facilitates quicker reaction times. Simulations demonstrate its scalability, affordability, and adaptability with a 93% detection rate, 5% false positive rate, and 120 millisecond response time.
Methods: Evaluate real-time cloud threat monitoring using simulated cloud environments by integrating the Immune Cloning Algorithm with d-TM.
Objectives: Reduce false positives, enhance scalability, proactive threat mitigation, and improve threat detection.
Results: 93% detection rate, 5% false positive rate, and 120 ms response time, outperforming conventional techniques like CSA and NLP.
Conclusion: The hybrid approach greatly improves cloud security by offering a proactive, flexible, and scalable solution. Future research will concentrate on edge computing and quantum computing extensions.