相关实验视频
在物联网中以人工智能驱动的网络安全:通过TRIM-SEC框架通过自适应式恶意软件检测和轻量级加密
1Department of Computer Science and Engineering, College of Applied Studies, King Saud University, Riyadh P.O. Box 11451, Saudi Arabia.
Sensors (Basel, Switzerland)
|November 27, 2025
概括
本研究介绍了TRIM-SEC,这是物联网 (IoT) 安全的新框架. 它提供高效的恶意软件检测和安全的数据传输,这对于保护智能生态系统免受先进的网络威胁至关重要.
科学领域:
- 网络安全 网络安全
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
- 机器学习 机器学习
背景情况:
- 物联网设备面临越来越复杂的恶意软件和零日攻击.
- 传统的安全方法对于资源有限的物联网系统是不够的.
- 实时威胁检测和安全通信对物联网至关重要.
研究的目的:
- 提出TRIM-SEC,一个轻量级和可扩展的框架,用于统一的威胁检测和物联网中的安全数据传输.
- 为了提高恶意软件检测的准确性,并减少物联网环境中的错误报警.
- 为下一代物联网生态系统提供高效和安全的通信.
主要方法:
- 基于自动编码器的特征删除 (AEFD) 用于提高数据质量.
- 主要组件分析 (PCA) 用于缩小维度.
- 变压器增强神经网络 (TANN) 用于使用自我注意的恶意软件分类.
- 轻量圆曲线加密 (LECC) 与粒子集群优化 (PSO) 进行密钥生成.
主要成果:
- 与基线模型相比,TRIM-SEC显示了更高的检测准确性.
- 该框架实现了虚假警报的减少.
- 观察到更低的加密延迟,适合实时物联网应用程序.
- TRIM-SEC平衡了检测性能,加密强度和计算效率.
结论:
- TRIM-SEC是一个有前途的解决方案,用于保护物联网环境免受先进的网络威胁.
- 该框架的轻量级和可扩展的设计非常适合资源有限的物联网系统.
- TRIM-SEC有效地将智能威胁检测与智能生态系统的安全数据传输统一起来.
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