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对于物联网边缘平台的入侵检测组合技术

Abdulaziz Aldaej¹, Imdad Ullah², Tariq Ahamed Ahanger³

¹College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University, Al-Kharj, 11942, Saudi Arabia. a.aldaej@psau.edu.sa.

Scientific reports

|May 22, 2024

概括

本研究介绍了用于物联网 (IoT) 安全的两阶段入侵检测系统. 这种新的方法通过有效识别和分析物联网环境中的网络威胁来增强网络防御.

科学领域:

计算机科学计算机科学
网络安全网络安全
网络工程网络工程

背景情况:

物联网 (IoT) 技术的采用由于设备资源限制而带来了重大安全挑战.
有效的入侵检测系统对于缓解互联物联网生态系统中的网络攻击至关重要.
现有的安全措施往往难以充分解决物联网网络的独特漏洞.

研究的目的:

为物联网 (IoT) 环境开发和评估一个强大的两阶段入侵检测程序.
提高识别和分类物联网数据流量的网络入侵的准确性和效率.
提供可靠的安全框架,解决资源有限的物联网设备中被忽视的安全问题.

主要方法:

提出了一种两阶段的入侵检测方法,从额外树 (E-Tree) 二进制分类器开始.
第二阶段采用集体技术 (ET) 集成E-Tree,深度神经网络 (DNN) 和随机森林 (RF) 进行侵入性事件分析.
通过使用各种数据集严格评估性能:Bot-IoT,CICIDS2018,NSL-KDD和IoTID20.

主要成果:

拟议的两阶段入侵检测策略与现有的机器学习方法相比,表现优越.
该系统实现了增强的统计指标,包括准确性,规范准确性,回忆和稳定性.
实验验证证了集成的E-Tree,DNN和RF整体方法在检测物联网入侵方面的有效性.

结论:

开发的两级入侵检测系统在保护物联网 (IoT) 网络方面取得了重大进展.
额外树和集体技术的组合提供了一种强大而准确的方法来识别复杂的网络威胁.
这项研究突出了先进的机器学习模型在解决不断发展的物联网环境中的关键安全漏洞方面的潜力.

关键词:

边缘计算是一种边缘计算.组合技术组合技术组合技术物联网的物联网,就是物联网.入侵检测系统的入侵检测系统安全的安全的安全的安全的安全.

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