在联合物联网网络中,LSTM-JSO框架用于保护隐私的自适应入侵检测
Shaymaa E Sorour1, Mohammed Aljaafari2, Amany M Shaker3
1Department of Management Information Systems, School of Business, King Faisal University, Al-Ahsa, 31982, Saudi Arabia. ssorour@kfu.edu.sa.
Scientific reports
|April 2, 2025
概括
本研究介绍了一种用于物联网 (IoT) 入侵检测的新型去中心化框架,增强了对复杂网络威胁的安全性. 它使用联合学习和动态优化来实现强大,私有和自适应的物联网安全.
科学领域:
- 网络安全 网络安全
- 计算机网络 计算机网络
- 人工智能的人工智能
背景情况:
- 物联网 (IoT) 系统面临越来越多的网络安全漏洞,原因是复杂的分布式攻击,如尸网络和分布式拒绝服务 (DDoS).
- 传统的集中式入侵检测系统与物联网环境的规模和动态性质作斗争,通常依赖于易于绕过的静态配置.
- 适应性,保护隐私和强大的安全解决方案的需求对于智能环境的安全扩散至关重要.
研究的目的:
- 引入一种新的去中心化框架,以加强物联网 (IoT) 系统的入侵检测.
- 提高入侵检测模型的适应性和稳定性,以应对异质物联网网络中不断变化的网络威胁.
- 通过协作,分散式学习,确保数据隐私并减少对数据中毒的脆弱性.
主要方法:
- 为物联网环境开发一个分散的入侵检测框架.
- 使用联合战略优化 (JSO) 算法进行动态超参数优化,以实现高效的模型适应.
- 实现基于长短期内存 (LSTM) 的架构,以捕获复杂的入侵模式,并与变压器,双LSTM和GRU模型进行验证.
- 在多个物联网网络中利用联合学习进行保护隐私的协作模式培训.
主要成果:
- 拟议的框架展示了物联网系统中增强的入侵检测能力.
- 动态优化确保在多样化和异质化的物联网网络中高效地适应模型.
- 联合学习有效地实现了协作培训,同时保护数据隐私和减轻毒害攻击.
- 使用各种架构的验证证实了该模型对复杂的网络威胁的稳定性和适应性.
结论:
- 这种新型的去中心化框架为物联网网络安全提供了强大的,适应性的解决方案.
- 该框架有效地解决了针对先进分布式攻击的集中式系统的局限性.
- 这种方法具有很大的潜力,可以保护物联网生态系统的快速扩张环境免受新出现的威胁.
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