合成数据集的大型语言模型 创建网络安全指标 妥协的指标
Ashwaq Almorjan1, Mohammed Basheri1, Miada Almasre1
1Department of Information Technology, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia.
Sensors (Basel, Switzerland)
|May 14, 2025
概括
这项研究对大型语言模型 (LLM) 进行了微调,以生成合成网络威胁情报数据集,提高网络安全应用程序的破坏指标 (IoC) 分类准确性.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 具有妥协指标 (IoC) 的高质量标记数据集很少,这阻碍了有效的网络威胁情报 (CTI) 预测模型的开发.
- 社交媒体平台越来越多地成为目标,需要在这些环境中使用强大的IoC分类方法.
研究的目的:
- 微调OpenAI的GPT-3.5大型语言模型 (LLM) 以生成模仿真实社交媒体数据的合成数据集.
- 将特定领域的IoC知识纳入LLM,以增强合成数据生成.
- 在这些合成数据集上评估机器学习和深度学习模型的性能.
主要方法:
- 微调GPT-3.5 LLM在一个精心策划的社交媒体数据集上与IoC领域的知识.
- 生成两个合成数据集 (4,000个和12,000个实例).
- 在生成的数据集上评估四个ML/DL模型 (DenseNN,物流回归).
主要成果:
- 在4000个实例数据集中,密集神经网络 (DenseNN) 达到77%的最高准确率.
- 在12000个实例数据集中,逻辑回归 (LR) 获得了82%的最高准确率.
- 该研究证明了微调的LLM在CTI中合成数据生成的有效性.
结论:
- 微调LLM与领域知识是创建高质量的合成数据集的可行策略.
- 生成的数据集可以改善 IoC 提取和分类,为网络安全提供新的资源.
- 这种方法解决了CTI研究和应用中的数据稀缺性挑战.
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