一个基于特征选择和大型语言模型微调的物联网入侵检测框架.
Huan Ma1,2, Wan Zhang3, Dalong Zhang4
1School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, 450001, China. mahuanresearch@126.com.
Scientific reports
|July 2, 2025
概括
这项研究引入了用于检测物联网 (IoT) 网络入侵的新框架. 它有效地减少了多余的功能,并使用人工智能生成更多的攻击数据,提高了安全性和效率.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 网络安全 网络安全
背景情况:
- 物联网 (IoT) 设备增加了网络漏洞.
- 现有的入侵检测系统 (IDS) 面临特征冗余和类不平衡的问题.
- 当前的方法往往孤立地解决这些挑战.
研究的目的:
- 提出基于特征选择和大型语言模型 (LLM) 的新型物联网入侵检测框架 (FSLLM).
- 在物联网入侵检测中解决特征冗余和类不平衡的问题.
- 提高物联网安全系统的效率和准确性.
主要方法:
- 一个结合最小冗余最大相关性 (mRMR) 和皮尔森相关系数 (PCC) 的多阶段特征选择算法,改进了共变矩阵适应演化策略 (CMA-ES).
- 微调大型语言模型 (LLM) 使用选定的功能来生成合成攻击样本.
- 使用焦点损失 (FL) 功能改进的LightGBM分类器来改进检测.
主要成果:
- 该FSLLM框架显著减少多余的功能超过80%.
- 在五个基准物联网数据集中实现与最先进的方法相比或更高的准确性.
- 证明有效生成更高质量的攻击样本,减轻类不平衡.
结论:
- 拟议的FSLLM框架为物联网入侵检测提供了一个有效的解决方案.
- 它成功地解决了功能冗余和类不平衡问题,从而提高了安全性.
- FSLLM 提供了一种计算效率高,准确的方法来加强物联网网络防御.
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