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对物联网安全的混合方法:结合集体学习与模糊逻辑逻辑
1Department of Computer Engineering, Zonguldak Bulent Ecevit University, 67100 Zonguldak, Türkiye.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
本研究介绍了一种使用集体学习和模糊逻辑来检测物联网 (IoT) 设备中的恶意软件的混合方法. 这种新的方法通过高精度和可解释的评估来提高物联网安全性.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 物联网的物联网,就是物联网.
背景情况:
- 物联网 (IoT) 设备的扩散,由于它们的多样性和资源有限性,带来了重大安全挑战.
- 传统的安全方法不足以有效打击日益增长的针对物联网生态系统的恶意软件威胁.
- 恶意软件对相互连接的物联网系统的完整性和功能构成重大风险.
研究的目的:
- 为物联网 (IoT) 系统提出一种新的混合安全框架.
- 在资源有限的物联网环境中提高恶意软件检测的准确性和可解释性.
- 开发一个强大的解决方案,解决各种物联网生态系统固有的网络安全漏洞.
主要方法:
- 整合合体学习技术,将多个分类器结合起来,以提高检测准确度.
- 模糊逻辑的应用,以灵活和人为直观的评估物联网系统安全状态.
- 开发一种混合框架,利用集体学习的预测能力和模糊逻辑的可解释性.
主要成果:
- 拟议的框架实现了对物联网设备的高精度恶意软件检测率.
- 综合模糊系统提供了更灵活和以人为本的安全状况评估.
- 实验验证证明了混合方法在各种物联网场景中的有效性.
结论:
- 这种新的混合方法在保护物联网 (IoT) 设备免受恶意软件攻击方面取得了重大进展.
- 集合学习和模糊逻辑的结合为物联网网络安全提供了强大而可解释的解决方案.
- 本研究提出了一种实用且适用的方法,用于增强各种物联网生态系统的安全性.
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