In the rapidly evolving landscape of cyber security, intelligent chatbots are gaining prominence. Artificial Intelligence, Machine Learning, and Natural Language Processing empower these chatbots to handle user inquiries and deliver threat intelligence. This helps cyber security knowledge readily available to both professionals and the public. Traditional rule-based chatbots often lack flexibility and struggle to adapt to user interactions. In contrast, Large Language Model-based chatbots offer contextually relevant information across multiple domains and adapt to evolving conversational contexts. In this work, we develop IntellBot, an advanced cyber security Chatbot built on top of cutting-edge technologies like Large Language Models and Langchain alongside a Retrieval-Augmented Generation model to deliver superior capabilities. This chatbot gathers information from diverse data sources to create a comprehensive knowledge base covering known vulnerabilities, recent cyber attacks, and emerging threats. It delivers tailored responses, serving as a primary hub for cyber security insights. By providing instant access to relevant information and resources, this IntellBot enhances threat intelligence, incident response, and overall security posture, saving time and empowering users with knowledge of cyber security best practices. Moreover, we analyzed the performance of our copilot using a two-stage evaluation strategy. We achieved BERT score above 0.8 by indirect approach and a cosine similarity score ranging from 0.8 to 1, which affirms the accuracy of our copilot. Additionally, we utilized RAGAS to evaluate the RAG model, and all evaluation metrics consistently produced scores above 0.77, highlighting the efficacy of our system.

翻译：在快速演进的网络安全领域，智能聊天机器人正日益受到重视。人工智能、机器学习和自然语言处理技术赋能这些聊天机器人处理用户查询并传递威胁情报，使网络安全知识能够便捷地为专业人士和公众所用。传统的基于规则的聊天机器人往往缺乏灵活性，难以适应用户交互。相比之下，基于大型语言模型的聊天机器人能够提供跨多个领域的上下文相关信息，并适应不断变化的对话语境。本研究开发了IntellBot，这是一款先进的网络安全聊天机器人，其构建于大型语言模型、Langchain等前沿技术之上，并结合检索增强生成模型以提供卓越能力。该聊天机器人从多样化数据源收集信息，构建涵盖已知漏洞、近期网络攻击及新兴威胁的综合性知识库，通过提供定制化响应，成为网络安全洞察的核心枢纽。通过即时访问相关信息和资源，IntellBot增强了威胁情报分析、事件响应及整体安全态势，在节省时间的同时赋能用户掌握网络安全最佳实践。此外，我们采用两阶段评估策略分析了该智能助手的性能：通过间接方法获得高于0.8的BERT分数，以及0.8至1区间的余弦相似度分数，验证了智能助手的准确性。同时，我们运用RAGAS框架评估检索增强生成模型，所有评估指标均持续获得高于0.77的分数，彰显了系统的有效性。