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arXiv 2510.08084cs.CRcs.AIcs.LG
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为增强物联网攻击检测的新型集成学习方法:重新定义连接系统中的安全范式

A Novel Ensemble Learning Approach for Enhanced IoT Attack Detection: Redefining Security Paradigms in Connected Systems

Department of Computer Science, Faculty of Information Technology, Applied Science Private University(计算机科学系,信息科技学院,应用科学私立大学);Department of Computer Science and Engineering, State University of Bangladesh(计算机科学与工程系,孟加拉国国家大学);Department. of Computer Science, College of Computer Engineering and Sciences, Prince Sattam Bin Abdulaziz University(计算机科学系,计算机工程与科学学院,普林斯萨塔姆·本·阿卜杜勒阿齐兹大学);School of Computer Science and Engineering, Lovely Professional University(计算机科学与工程学院,洛丽专业大学);STC’s Artificial Intelligence Chair, Department of Information Systems, College of Computer and Information Sciences, King Saud University(STC人工智能 chair,信息系,计算机与信息科学学院,沙特国王大学);Department of Computer Science, Veer Kunwar Singh University(计算机科学系,维尔·库纳尔·辛格大学);Department of Computer Science and Engineering, School of Engineering, Kathmandu University(计算机科学与工程系,工程学院,加德满都市大学)

Hikmat A. M. Abdeljaber, Md. Alamgir Hossain, Sultan Ahmad, Ahmed Alsanad, Md Alimul Haque, Sudan Jha, Jabeen Nazeer

AI总结:

本文提出一种新型集成学习方法,通过Extra Trees Classifier和预处理优化,提升物联网攻击检测性能,实验表明其在多个数据集上表现出高召回率、准确率和低错误率。

摘要:

物联网设备的快速扩展已通过广泛连接和数据交换改变了工业和日常生活。然而,这种增加的互联性引入了严重的安全漏洞,使物联网系统更容易受到复杂网络攻击。本研究提出了一种新型集成学习架构,旨在改进物联网攻击检测。所提出的方法应用了先进的机器学习技术,特别是Extra Trees Classifier,结合彻底的预处理和超参数优化。该方法在多个基准数据集上进行评估,包括CICIoT2023、IoTID20、BotNeTIoT L01、ToN IoT、N BaIoT和BoT IoT。结果表明性能优异,实现了高召回率、准确率和精度,同时具有极低的错误率。这些成果展示了模型的效率和优越性,相比现有方法提供了有效且可扩展的安全方法。本研究为未来保护连接设备免受演变网络威胁奠定了坚实基础。

英文摘要:

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.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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