Host-Based Intrusion Detection Systems Powered By Large Language Models

Brief Description

SHIELD leverages a customized large language model pipeline to detect and investigate sophisticated cyber threats with high accuracy and interpretability.

Full Description

SHIELD is an innovative host-based intrusion detection system (HIDS) that integrates advanced large language model (LLM) techniques with semantic reasoning and behavioral profiling to analyze fine-grained system logs. It addresses common challenges in traditional HIDS such as high false-positive rates and inconsistent results by employing a tailored LLM pipeline featuring event-level Masked Autoencoders, deterministic data augmentation, and multi-purpose prompting. This results in precise detection and interpretable attack investigations across diverse computing environments.

Suggested uses

• Enterprise security operations centers (SOCs) for advanced threat detection 
• Research environments focusing on cybersecurity and intrusion detection 
• Organizations requiring protection across diverse platforms like Linux and Windows 
• Enhancement of existing HIDS deployments through AI-driven analysis 
• Security benchmarking and testing of host activity datasets

Advantages

• High accuracy in detecting advanced persistent threats (APT) and insider threats 
• Robust across various operating systems and log datasets 
• Reduced false positives and enhanced interpretability for analysts 
• Integrates semantic analysis and behavioral profiling for comprehensive threat detection 
• Supports both real-time and retrospective intrusion detection 
• Automates generation of detailed attack narratives to aid security triage