相关实验视频
用LSTM指导的同型加密,用于对威胁有抵抗力的物联网网络
Sanjeev Kumar1, Sukhvinder Singh Deora1, Tajinder Kumar2
1Department of Computer Science & Application, Maharshi Dayanand University, Rohtak, India.
概括
NeuroCrypt通过将完全同型加密 (FHE) 与LSTM异常检测相结合,增强物联网 (IoT) 安全性. 这种保护隐私的系统为物联网网络提供实时威胁减缓.
科学领域:
- 网络安全 网络安全
- 网络安全 网络安全
- 数据 隐私 数据 隐私 数据
背景情况:
- 物联网 (IoT) 面临着严重的安全和隐私挑战,因为它的分布式性质和资源限制.
- 传统密码学和机器学习等现有解决方案缺乏实时,集成的数据隐私和威胁弹性.
- 同型加密 (HE) 在计算上昂贵,而长短期内存 (LSTM) 网络预测异常,而不是加密数据.
研究的目的:
- 提出NeuroCrypt,一种用于实时,保护隐私的物联网网络威胁检测的新型混合系统.
- 通过整合先进的加密,异常检测和安全管理技术来解决现有方法的局限性.
主要方法:
- NeuroCrypt 结合了完全同型加密 (FHE) 和基于 LSTM 的加密异常检测.
- 它结合了区块链,用于动态密钥管理和多因素身份验证.
- 该架构被优化为边缘和雾计算,使用密码文本包装,模型量化和并行加密操作等技术.
主要成果:
- 拟议的NeuroCrypt框架在真实数据集上实现了99.2%的准确性.
- 对现有方法进行了性能评估,包括基于HE的深度神经网络 (DNN),联合学习 (FL) 模型和LSTM入侵检测系统 (IDS).
结论:
- 在物联网环境中,NeuroCrypt提供了一种保护隐私,有效和可扩展的解决方案,用于实时降低物联网环境中的威胁.
- 混合方法成功地整合了加密,异常检测和强大的安全管理,以提高物联网安全性.
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