SecEdge:一个新的深度学习框架,用于移动物联网环境中的实时网络安全
Kamran Ahmad Awan1, Ikram Ud Din1, Ahmad Almogren2
1Department of Information Technology, The University of Haripur, Haripur, 22620, Khyber Pakhtunkhwa, Pakistan.
Heliyon
|January 13, 2025
概括
本研究介绍了SecEdge,这是物联网 (IoT) 网络安全的深度学习框架. SecEdge有效地实时检测各种网络威胁,改善移动物联网安全性.
科学领域:
- 网络安全 网络安全
- 深度学习 (Deep Learning) 是一种深度学习.
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 多样化,资源有限的物联网 (IoT) 设备的扩散给网络安全带来了重大挑战.
- 当前的安全解决方案很难解决物联网环境的动态和分布式性质.
- 实时威胁检测和数据隐私对于移动物联网系统至关重要.
研究的目的:
- 提出SecEdge,一个新的深度学习框架,用于在移动物联网环境中增强实时网络安全.
- 整合先进的人工智能技术,以进行强大的威胁检测和缓解.
- 确保数据隐私并减少物联网安全中的延迟.
主要方法:
- 集成基于变压器的模型来进行依赖性分析,以及用于关系数据的图形神经网络 (GNN).
- 实施联合学习以实现分散培训和数据隐私.
- 开发一种适应性学习机制,用于对不断变化的威胁进行持续的模型更新.
主要成果:
- 在多个基准数据集 (NSL-KDD,UNSW-NB15,CICIDS2017) 中,SecEdge表现出卓越的性能.
- 实现了高的检测率:98.8%的DoS攻击 (NSL-KDD),98.5%的MitM攻击 (UNSW-NB15) 和98.7%的数据注入攻击 (CICIDS2017).
- 在模拟环境中超越现有的最先进的网络安全方法.
结论:
- SecEdge为移动物联网中的实时网络安全提供了一个有前途的解决方案.
- 该框架的混合深度学习方法有效地应对复杂的网络威胁.
- 联合学习和自适应机制提高了物联网安全的可扩展性和弹性.
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