计算机应用与软件2025，Vol.42Issue(5)：179-190,12.DOI:10.3969/j.issn.1000-386x.2025.05.025
基于安全大模型的网络安全威胁检测框架研究
CYBERSECURITY THREAT DETECTION FRAMEWORK BASED ON SECURITYGPT
李橙 1陈铭丰 2苏嘉珺 2杨磊 1梁海航3
作者信息
- 1. 上海市公安局虹口分局 上海 200000
- 2. 上海市公安局 上海 200000
- 3. 深信服科技股份有限公司 上海 200000
- 折叠
摘要
Abstract
In response to the challenges in the field of cybersecurity risk detection,such as the difficulty in pinpointing genuine attacks,low efficiency in risk assessment,judgment and disposal,and the high technical requirements for security personnel,a deep threat detection framework based on a SecurityGPT is proposed.This paper constructed a high-performing generative artificial intelligence large model tailored for the vertical domain of cybersecurity through corpus construction,model pre-training,instruction fine-tuning,and model inference acceleration.On this foundation,to further enhance the accuracy and detection efficiency of the model,a multi-dimensional collaborative research was conducted focusing on the integration of the security large model with traditional rule-based models and small-scale machine learning models.This initiative aimed to establish a tripartite deep threat detection architecture and tested in actual business environments.Experimental results show that this framework can ensure an average network risk detection rate of over 95％with a false positive rate below 5％,while significantly improving detection efficiency and reducing labor costs,demonstrating excellent application value.关键词
网络安全/安全大模型/生成式人工智能/模型推理加速/模型协同Key words
Cybersecurity/SecurityGPT/Generative artificial intelligence/Model inference acceleration/Model collaboration分类
计算机与自动化引用本文复制引用
李橙,陈铭丰,苏嘉珺,杨磊,梁海航..基于安全大模型的网络安全威胁检测框架研究[J].计算机应用与软件,2025,42(5):179-190,12.
