Large Language Models for Cybersecurity

Project Overview

This cutting-edge research explores the application of large language models (LLMs) for cybersecurity applications, particularly in threat intelligence and security text analysis. The project addresses the unique challenges of applying LLMs to security domains while maintaining accuracy and interpretability.

Research Contributions

• LLM Adaptation for Security: Developed techniques for fine-tuning large language models for cybersecurity-specific tasks
• Threat Detection: Created models for identifying malicious content and security threats in text data
• Interpretability: Established methods for explaining LLM decisions in security contexts
• Adversarial Robustness: Investigated and improved LLM resilience against adversarial attacks

Key Innovations

• Novel prompt engineering techniques for security applications
• Multi-modal approaches combining text and structured security data
• Real-time threat assessment using transformer-based models
• Framework for evaluating LLM performance in security contexts

Publications & Impact

• Published in ACM Transactions on Management Information Systems
• Presented at leading AI and cybersecurity conferences
• Influenced industry adoption of LLMs for security
• Generated significant academic and industry interest

Technical Stack

• PyTorch, Transformers, Hugging Face
• Large Language Models (GPT, BERT, RoBERTa)
• Security datasets and threat intelligence feeds
• Cloud computing infrastructure (AWS, Azure)
• MLOps and model deployment pipelines