相关实验视频
物联网设备网络的安全审计:用于威胁检测和性能基准测试的可重复机器学习框架
Aigul Shaikhanova1, Oleksandr Kuznetsov2,3, Aizhan Tokkuliyeva1
1Department of Information Security, L.N. Gumilyov Eurasian National University, 2, Satpayeva St., Astana 010000, Kazakhstan.
Sensors (Basel, Switzerland)
|December 31, 2025
概括
本研究介绍了物联网 (IoT) 网络的可重复性安全审计框架,在检测威胁方面实现了高准确性,同时确保了计算效率和透明度,以便更好地评估物联网安全性.
科学领域:
- 网络安全 网络安全
- 网络安全 网络安全
- 机器学习应用 机器学习应用
背景情况:
- 物联网 (IoT) 部署越来越容易受到安全威胁.
- 现有的入侵检测评估优先考虑算法准确性,而不是效率和可重复性等操作需求.
- 对物联网网络防御的系统审计方法尚不发达.
研究的目的:
- 为物联网设备网络引入可重复的安全审计框架.
- 评估用于物联网威胁检测的机器学习模型的性能.
- 根据审计结果,提供可执行的安全建议.
主要方法:
- 为物联网网络开发了一个可重复的安全审计框架.
- 在TON_IoT数据集上评估了四种机器学习模型 (随机森林,LightGBM,XGBoost,物流回归).
- 排除了用于特征卫生和对计算成本进行基准检测能力的身份揭示属性.
- 执行二进制和多类分类,用于威胁检测和归因.
主要成果:
- 集成模型在二进制分类中实现了99.8-99.9%的准确性,具有100%的ROC-AUC和低推理延迟.
- 多类审计达到99.0%的准确性,但在检测罕见的攻击类型中发现了漏洞,例如中间人 (78%F1).
- 轻GBM表现出最佳的性能,平衡高检测精度 (99.93%) 与小部署足迹 (2.76 MB).
结论:
- 拟议的框架为生产物联网网络提供透明,高效和可重复的安全评估.
- 可行的建议包括网络细分,速率限制和TLS元数据收集.
- 该框架实现了具有竞争力的检测率,同时解决了安全审计的关键操作要求.
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