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在IoMT中提高安全性,使用联合的TinyGAN进行轻量级和准确的恶意软件检测
Durga S1, M Gobi Shankar2, Esther Daniel3
1TIFAC CORE in Cyber Security, Amrita School of Engineering, Amrita Vishwa Vidyapeetham, Coimbatore, 641112, India. s_durga@cb.amrita.edu.
Scientific reports
|February 3, 2026
概括
这项研究介绍了一个联合学习 (FL) 框架与TinyGAN用于医疗物联网 (IoMT) 恶意软件检测. 该方法通过实现去中心化学习和合成数据生成来提高准确性和隐私,从而提高安全性.
科学领域:
- 网络安全 网络安全
- 机器学习 机器学习
- 物联网的物联网,就是物联网.
背景情况:
- 医疗物联网 (IoMT) 面临着严重的恶意软件威胁,因为许多连接的设备处理敏感数据.
- 不充分的设备管理会产生漏洞,允许恶意软件在最小的检测下快速传播.
研究的目的:
- 为资源有限的IoMT设备提出一个轻量级和自适应的入侵检测框架.
- 通过使用联合学习和合成数据生成,提高IoMT环境中的恶意软件检测准确性和效率.
主要方法:
- 开发与TinyGAN集成的联合学习 (FL) 框架,用于生成合成数据以改善恶意软件检测.
- 分散式,持续学习的方法,以适应新出现的威胁,而无需集中再培训,维护隐私和减少计算负载.
主要成果:
- 在20轮训练后,FL-TinyGAN框架实现了高性能指标:99.30%的精度,100%的回忆率和99.52%的F1得分.
- 对IoT-23数据集的实验分析表明,FL与TinyGAN在准确性,收性和资源效率方面超过了传统模型 (MLP,FNN/LSTM).
结论:
- 拟议的FL-TinyGAN框架为保护IoMT环境免受恶意软件攻击提供了一个可扩展,保护隐私和有效的解决方案.
- 在物联网恶意软件检测方面取得了重大实际进展,在关键绩效指标上表现优于传统方法.
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