在一群架构中对机器学习模型进行编排,用于物联网内联恶意软件检测
Muhammad Hanif1, Ehsan Ullah Munir1, Muhammad Maaz Rehan1,2
1COMSATS University Islamabad, G.T Road, Wah Cantt, Islamabad, Pakistan.
Scientific reports
|December 20, 2025
概括
这项研究介绍了基于Swarm的Inline机器学习 (SIML),以提高物联网 (IoT) 对网络威胁的安全性. SIML有效地检测恶意软件,实现高准确度和精度,为更安全的数字未来.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 物联网 (IoT) 生态系统正在快速扩展,增加对网络威胁的易感性.
- 物联网设备的漏洞使它们成为恶意行为者的首要目标,构成重大安全风险.
- 现有的独立威胁检测系统不足以保护复杂的物联网环境.
研究的目的:
- 为增强物联网安全引入创新的基于Swarm的在线机器学习 (SIML) 方法.
- 开发一个分布式和端到端的安全解决方案,以应对物联网环境中新出现的恶意软件威胁.
- 为了减少物联网设备被利用进行网络攻击的风险.
主要方法:
- 拟议的Swarm-based Inline Machine Learning (SIML) 方法利用了协调的群体数据处理.
- 梯度增强树算法在SIML框架内用于威胁检测.
- 使用UNSW-NB15,BoT-Iot和Edge-IIoTset数据集进行了严格的测试.
主要成果:
- 在UNSW-NB15数据集上,SIML方法实现了93.7%的准确率和95%的精确率.
- 梯度提升算法在线设置中表现出优越的性能,与传统方法相比.
- 该方法在BoT-Iot和Edge-IIoTset数据集上表现出优势,在更高的吞吐量时有轻微的退化.
结论:
- SIML提供了一种强大,分布式和有效的解决方案,用于保护物联网环境免受网络威胁.
- 拟议的方法显著提高了物联网设备的安全性,减少了它们对利用的脆弱性.
- 这项研究通过先进的物联网安全措施,为更安全,更有弹性的数字未来做出了贡献.
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