CYBERVIDYA: Rag Infused Cyber Solutions

Abstract

As cyber threats grow more sophisticated, the need for intelligent, adaptive security solutions has never been greater. CyberVidya: RAG-Infused Cyber Solutions offers a groundbreaking approach by integrating large language models (LLMs) with retrieval-augmented generation (RAG) to provide precise, real-time cybersecurity insights. Unlike traditional models that rely on static knowledge, CyberVidya continuously retrieves and processes information from a dynamic, indexed database of academic papers, ethical books, PDFs, and real-world case studies. What sets CyberVidya apart is its Non-Parametric Knowledge Retrieval, which ensures that responses are contextually accurate and directly sourced from trusted materials. Its multidimensional query-optimized retrievers work alongside advanced LLMs—GPT-2, Mistral-7B, and Llama 3.2-3B—to generate reliable, actionable insights. By incorporating document embedding and Dense Passage Retrieval (DPR), CyberVidya enhances accuracy while adapting to the ever-changing cybersecurity landscape without the need for retraining. The results speak for themselves. CyberVidya consistently outperforms industry-leading models, achieving 92.86% relevance, 85.81% similarity, and 95.06% correctness in educational queries. For scenario-based cybersecurity challenges, it maintains high performance with 92.89% relevance, 89.56% similarity, and 93.94% correctness. Comparative studies further highlight that RAG-based models surpass traditional LLMs in understanding complex cybersecurity concepts such as tactics, techniques, and procedures (TTPs) With its ability to provide accurate, real-time cybersecurity guidance, CyberVidya stands as a powerful tool for individuals, enterprises, and educators, bridging the gap between static knowledge and dynamic problem-solving.