通过物联网设备类型来加强物联网安全性 使用优化的变量自动编码器进行识别 Wasserstein 生成对立网络
Jothi Shri Sankar1, Saravanan Dhatchnamurthy2, Anitha Mary X3
1Department of Computer Science and Engineering, Saveetha School of Engineering, SIMATS, Chennai, Tamil Nadu, India.
本研究引入了一种用于识别物联网 (IoT) 设备的新方法,以提高网络安全性. 与现有的物联网设备类型识别技术相比,提出的方法显著提高了准确性并降低了错误率.
科学领域:
- 网络安全 网络安全
- 网络安全 网络安全
- 机器学习 机器学习
背景情况:
- 物联网 (IoT) 设备的扩散需要强大的网络识别,授权和攻击保护方法.
- 现有的安全措施很难跟上不断扩大的物联网生态系统的步伐.
研究的目的:
- 为物联网设备类型识别提出一种新的方法,以延长物联网安全性.
- 提高在网络上区分已知与未知的物联网设备的准确性和效率.
主要方法:
- 一个三阶段的方法:从各种物联网设备收集数据,使用自适应和简洁的经验波形变换提取特征,以及设备类型检测.
- 使用一个变量自动编码器瓦斯斯坦生成对抗网络 (VAWGAN) 进行分类.
- 通过 Pelican 优化算法 (POA) 优化 VAWGAN 的重量因子,以提高性能.
主要成果:
- 拟议的物联网-DTI-VAWGAN-POA方法在实验中表现出卓越的性能.
- 与现有方法相比,在准确度方面取得了显著的改进 (比如33.41%更高),并减少了错误率 (比如44.78%更低).
- 评估指标包括准确性,精度,f-测量,灵敏度,错误率,计算复杂性和接收器操作特征 (RoC) 曲线分析.
结论:
- 物联网-DTI-VAWGAN-POA方法在保护物联网环境方面提供了一个有前途的进步.
- 优化算法有效地增强了用于设备识别的生成对抗网络的能力.
- 这些发现表明,连接设备的未来更安全,更可靠.
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