迈向安全的Metaverse:利用物联网异常检测的混合模型来实现物联网异常检测
Sanchit Vashisht1, Shalli Rani1, Mohammad Shabaz2
1Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India.
PloS one
|April 14, 2025
概括
结合随机森林 (RF) 和神经网络 (NN) 的新混合模型在启用物联网 (IoT) 的元宇宙环境中检测异常时达到99.99%的准确性,增强了系统安全性和信任.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 计算机网络 计算机网络.
背景情况:
- 物联网 (IoT) 和元宇宙的整合模糊了虚拟世界和现实世界,需要强有力的安全措施.
- 伦理,隐私和安全问题来自于在元宇宙中相互连接的物联网系统.
- 异常检测对于识别和防止复杂,动态物联网网络中的恶意活动至关重要.
研究的目的:
- 提出和评估一种混合机器学习模型,用于在支持物联网的元宇宙环境中检测异常.
- 将拟议的混合模型的性能与各种传统机器学习技术进行比较.
- 增强元宇宙中连接设备的安全性和可靠性.
主要方法:
- 开发了一个混合模型,集成随机森林 (RF) 和神经网络 (NN).
- 混合模型和其他几种机器学习算法 (决策树,天真贝叶斯,K-近邻,RF,物流回归) 被训练和测试.
- 在模型评估中使用了CIC-IDS 2017年网络入侵数据集.
主要成果:
- 拟议的混合RF-NN模型与其他机器学习模型相比,表现出优越的性能.
- 混合型号在异常检测方面实现了99.99%的异常准确率.
- 该研究强调了该模型在高精度异常识别和最大限度地减少假阳性的有效性.
结论:
- 混合RF-NN模型为在支持物联网的元宇宙系统中检测异常提供了高度有效的解决方案.
- 这种方法显著提高了连接设备的安全性和可靠性.
- 这些发现强调了先进机器学习在保护元宇宙环境方面的潜力.
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