一个轻量级的框架来保护在云环境中资源有限的物联网设备
Vivek Kumar Pandey1, Dinesh Sahu2, Shiv Prakash3
1Department of Electronics and Communication, University of Allahabad, Prayagraj, Uttar Pradesh, India.
Scientific reports
|July 17, 2025
概括
本研究介绍了一种用于物联网 (IoT) 设备的轻量级入侵检测系统. 它提供高精度和低能耗,以提高物联网安全性.
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:
- 由于资源有限,数十亿的物联网 (IoT) 设备容易受到网络威胁.
- 物联网设备经常充当云基础设施的门户,增加它们的攻击面.
- 物联网中的资源限制阻碍了强有力的安全措施的实施.
研究的目的:
- 为资源有限的物联网环境提出轻量级入侵检测框架.
- 为了实现物联网网络中的实时异常检测.
- 在不影响设备性能的情况下增强物联网安全性.
主要方法:
- 开发了一个基于决策树的轻量级入侵检测框架.
- 实施了一种新的叶子切割特征优化策略.
- 利用适应式云端智能进行高效的处理.
主要成果:
- 实现了高精度 (NSL-KDD上98.2%,Bot-IoT上97.9%) 误报率低于1%.
- 与传统模型 (如SVM,神经网络) 相比,在精度和能源效率方面 (减少多达78%的能源) 取得了显著的改进.
- 在Raspberry Pi节点上以1,250个样本/秒的速度在不到1毫秒的时间内实现实时推断,只有12.5MB的内存使用.
结论:
- 拟议的框架为物联网安全提供了一个节能,可扩展和可解释的解决方案.
- 适合在智能城市和医疗保健等各种物联网应用中实时检测异常.
- 解决了在资源有限的物联网生态系统中有效安全的关键需求.
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