人工智能驱动的物联网网络安全系统使用自我注意力深度学习和元启发算法
1Computer Science Department, Community College, King Saud University, 11437, Riyadh, Saudi Arabia. falblehi@ksu.edu.sa.
Scientific reports
|April 16, 2025
概括
本研究介绍了一种使用基于自我注意的深度学习和元启发式优化算法 (ICSSADL-MHOA) 的智能网络安全系统,以应对不断发展的物联网 (IoT) 网络威胁. 该新系统在检测和分类网络安全攻击方面达到99.37%的准确性.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 物联网 (IoT) 的物联网 (IoT) 的物联网.
背景情况:
- 物联网 (IoT) 设备的快速扩张导致了网络安全漏洞的增加.
- 由于设备的局限性,对物联网的网络攻击是独一无二的,需要专门的防御机制.
- 人工智能 (AI) 越来越多地用于网络安全,但对抗性AI带来了新的威胁.
研究的目的:
- 为强大的物联网安全提出一个智能网络安全系统,使用基于自我关注的深度学习和元启发式优化算法 (ICSSADL-MHOA).
- 提高物联网环境中网络安全威胁的检测和分类.
- 开发一种适应性和实时网络安全模型来应对先进的AI驱动攻击.
主要方法:
- 数据规范化使用min-max规范化以保持一致性.
- 通过改进的金枪鱼群优化 (ITSO) 进行特征选择.
- 使用双向长期短期记忆与自我注意 (BiLSTM-SA) 的网络安全威胁检测和分类,通过饥饿游戏搜索 (HGS) 进行优化.
主要成果:
- ICSSADL-MHOA模型在ToN-IoT和Edge-IIoT数据集上表现出卓越的性能.
- 在检测和分类网络安全威胁方面达到99.37%的高精度.
- 在实验验证中表现优于现有的网络安全技术.
结论:
- 拟议的ICSSADL-MHOA模型为增强物联网网络安全提供了有效的解决方案.
- 深度学习和元启发算法的集成提供了对复杂的网络威胁的强有力的防御.
- 这项研究有助于为不断变化的威胁格局开发先进的,适应性的网络安全系统.
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