Cultivating Cybersecurity Talent: Localized RAG Approach Integrating Pattern Recognition and Document Analysis

Cybersecurity threats are becoming increasingly complex and diverse. Cultivating applied professionals with practical skills has become a key task for higher education. This paper proposes an innovative talent development system, centered on localized Retrieval-Augmented Generation (RAG) technology and integrating multiple technologies to promote the deep integration of technology and teaching. First, a knowledge integration and update mechanism based on localized RAG is constructed to automatically integrate authoritative vulnerability libraries such as NIST NVD, CNVD, and CWE. Machine learning techniques are applied for data cleansing and feature extraction to ensure the continuous acquisition of timely and accurate knowledge, laying a solid foundation for teaching and case generation. Second, personalized case generation technology is applied. By combining pattern recognition and machine learning algorithms to analyze student competency models, learning history, and the latest threat intelligence, Large Language Models (LLMs) are used to dynamically generate targeted attack and defense scenario descriptions, reproduction steps, and detection/defense solutions based on deep learning methods. This system meets differentiated learning needs and effectively improves students’ ability to cope with real-world threat environments. Furthermore, a natural language interactive teaching support system is designed. Relying on knowledge base engines (such as AnythingLLM), it realizes multi-format document analysis (including PDF, Markdown, Word, etc.) and efficient ingestion and vectorized storage, and combines with the Ollama artificial intelligence big model for intelligent retrieval and text generation to enhance the comprehensibility and interactivity of teaching content. In knowledge base management, efficient document analysis, pattern recognition and vectorization technologies are used to ensure storage and retrieval efficiency, and private intelligent cloud solutions (such as Infortres) are used to achieve secure remote access to local data and artificial intelligence services to meet compliance requirements and provide convenient support. Practical verification shows that the system significantly improves students’ offensive and defensive practical ability and problem-solving ability, and provides teachers with efficient and flexible teaching methods. In summary, the system realizes the deep feedback of technology and education, and opens up a new path for the cultivation of application-oriented talents in network security. In the future, we will optimize the case generation strategy, expand the scope of the knowledge base, and explore more artificial intelligence teaching applications (such as adaptive learning path recommendation) to continuously improve educational effectiveness.