一种混合改进的二进制GWO-PSO与随机森林 (IBGWO-PSO-RF) 基于入侵检测模型,用于物联网环境中的大规模攻击
Abdulwahid Al Abdulwahid1, Saad Said Alqahtany2, Darakhshan Syed3
1Computer and Information Technology Department, Jubail Industrial College, Royal Commission for Jubail and Yanbu, 31961, Jubail Industrial City, Saudi Arabia.
Scientific reports
|January 6, 2026
概括
本研究介绍了一种用于物联网 (IoT) 安全的新型入侵检测系统 (IDS). 混合模型在检测物联网网络的多类攻击时达到99.96%的准确性.
科学领域:
- 网络安全 网络安全
- 网络安全 网络安全
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 物联网 (IoT) 环境提供了许多好处,但面临着重大的安全挑战.
- 传统的入侵检测系统 (IDS) 难以处理来自物联网网络的大量冗余数据,导致精度降低和响应时间减慢.
研究的目的:
- 提出一个增强的入侵检测系统 (IDS),专门为物联网 (IoT) 网络的独特挑战而设计.
- 提高物联网环境中攻击检测的准确性和效率.
主要方法:
- 利用CIC-IoT-2023数据集进行培训和评估拟议的IDS.
- 集成改进的二进制灰狼优化 (IBGWO) 和粒子群集优化 (PSO) 以从物联网网络数据中有效选择特征.
- 使用随机森林 (RF) 算法对检测到的威胁进行多类分类.
主要成果:
- 拟议的混合IBGWO-PSO-RF模型实现了99.96%的高度准确的多类分类率.
- 与现有的IDS算法相比,在检测物联网网络上的各种攻击方面表现出卓越的性能.
结论:
- 开发的混合IBGWO-PSO-RF模型为保护物联网 (IoT) 环境提供了强大而准确的解决方案.
- 这种方法有效地解决了传统IDS在处理大规模物联网数据以检测威胁方面的局限性.
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