LSTM-Enabled Big Data Security Framework Integrating Kerberos Authentication on AWS for Robust Cloud Protection

In today’s digital era, safeguarding cloud environments has become paramount due to the increasing complexity and volume of cyber threats. This research presents an innovative LSTM-enabled Big Data Security Framework that integrates the Kerberos Authentication Protocol on the Amazon Web Services (AWS) cloud platform. The proposed framework leverages the scalability and processing capabilities of Hadoop to manage and analyze large-scale data while ensuring robust security through Kerberos. The Long Short-Term Memory (LSTM) model is deployed to predict and detect anomalies in authentication requests, enhancing the framework's ability to mitigate unauthorized access. By combining LSTM’s predictive analytics with Kerberos’s ticket-based authentication, the framework ensures a multi-layered security architecture capable of identifying threats in real-time. Hadoop’s distributed computing environment further enables efficient processing of security logs and user behavior data, making it ideal for large-scale enterprise applications. A comprehensive evaluation of the framework demonstrates its efficacy in reducing false positives and achieving a high detection accuracy rate for potential threats. Key metrics, including precision, recall, and F1 score, validate the robustness of the approach. Additionally, the framework showcases superior scalability and adaptability to dynamic workloads in the AWS environment, ensuring consistent performance under varying data loads. This research contributes to the advancement of secure cloud computing by bridging the gap between traditional authentication methods and machine learning-driven threat detection. The integration of Kerberos with Big Data analytics and LSTM-based models establishes a foundation for future work in securing large-scale cloud ecosystems.