用集成学习提升物联网攻击检测，准确率高且可扩展。
A Novel Ensemble Learning Approach for Enhanced IoT Attack Detection: Redefining Security Paradigms in Connected Systems
- 融合Extra Trees等算法，结合数据预处理与超参优化
- 在6个基准数据集上实现高召回率、高精度与低误报率
- 适合需要实时防护的物联网安全系统部署
物联网设备的快速普及推动了产业与日常生活的连接化发展，但同时也带来了严重的安全漏洞，使系统面临日益复杂的网络攻击。本文提出一种新型集成学习架构，旨在提升物联网攻击检测能力。该方法采用Extra Trees分类器，结合全面的数据预处理与超参数优化，在多个基准数据集（CICIoT2023、IoTID20、BotNeTIoT L01、ToN IoT、N BaIoT、BoT IoT）上进行评估。实验结果表明，模型在各项指标上表现优异，具备高召回率、高准确率与高精确度，同时误差极低。相比现有方法，展现出显著效率与性能优势，为构建高效、可扩展的物联网安全防护体系提供了坚实基础。
原文摘要 · Abstract (English)
The rapid expansion of Internet of Things (IoT) devices has transformed industries and daily life by enabling widespread connectivity and data exchange. However, this increased interconnection has introduced serious security vulnerabilities, making IoT systems more exposed to sophisticated cyber attacks. This study presents a novel ensemble learning architecture designed to improve IoT attack detection. The proposed approach applies advanced machine learning techniques, specifically the Extra Trees Classifier, along with thorough preprocessing and hyperparameter optimization. It is evaluated on several benchmark datasets including CICIoT2023, IoTID20, BotNeTIoT L01, ToN IoT, N BaIoT, and BoT IoT. The results show excellent performance, achieving high recall, accuracy, and precision with very low error rates. These outcomes demonstrate the model efficiency and superiority compared to existing approaches, providing an effective and scalable method for securing IoT environments. This research establishes a solid foundation for future progress in protecting connected devices from evolving cyber threats.
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