LLMBA: Efficient Behavior Analytics via Large Pretrained Models in Zero Trust Networks

This paper presents LLMBA, a framework that uses Large Language Models (LLMs, AI systems trained on vast amounts of text) to detect unusual or malicious behavior in Zero Trust networks (security systems that continuously verify every user and device). The system uses self-supervised learning (training without requiring humans to manually label all the data) and knowledge distillation (a technique that compresses an AI model to use fewer resources while keeping it accurate) to efficiently identify both known and previously unseen threats in user activity logs.