User and Entity Behavior Analysis

RAM Laboratories' proposed UEBA solution addresses the U.S. Army's critical need for enhanced authentication and security within tactical networks. By leveraging the normalized data provided by the Army and employing state-of-the-art machine learning techniques, including autoencoders, Long Short-Term Memory (LSTM) networks, and Graph Neural Networks, our solution enables real-time anomaly detection, risk assessment, and proactive threat response. The seamless integration with the Army's existing Elastic Stack infrastructure ensures efficient deployment and operation. The Phase I proof of concept will demonstrate the feasibility and effectiveness of our approach, paving the way for the successful deployment of a comprehensive UEBA solution that significantly strengthens the cybersecurity posture of the Army's tactical networks. Key benefits of this solution are (1) real-time anomaly detection and risk assessment using state-of-the-art ML techniques, (2) seamless integration with existing Elastic Stack infrastructure, (3) scalable and performance-optimized for tactical network environments, (4) continuous adaptation to evolving threat landscapes, and (5) enables proactive threat response and minimizes breach impact.