开创先进的安全解决方案,用于基于强化学习的Zigbee网络中的自适应键旋转
Xiaofen Fang1,2, Lihui Zheng3, Xiaohua Fang4
1Faculty of Mechanical and Electrical Engineering, Quzhou College of Technology, Quzhou, 324000, Zhejiang, China. fangxiaofen@ieee.org.
Scientific reports
|June 17, 2024
概括
强化学习通过自适应旋转键来增强Zigbee网络安全性,在效率和抵御分布式拒绝服务 (DDoS) 等攻击的弹性方面超过传统方法. 这种由人工智能驱动的方法提高了物联网 (IoT) 的网络性能和安全性.
科学领域:
- 计算机科学 计算机科学
- 网络安全 网络安全
- 人工智能的人工智能
背景情况:
- 齐格比网络对于物联网 (IoT) 通信至关重要,但面临安全漏洞.
- 关键管理和应对分布式拒绝服务 (DDoS) 攻击的弹性是重大挑战.
- 传统的钥匙旋转方法缺乏动态适应不断变化的网络条件.
研究的目的:
- 提出和评估一种新的强化学习 (RL) 模型,用于Zigbee网络中的自适应键旋转.
- 加强网络安全,效率和应对网络攻击的弹性.
- 将RL模型的性能与传统的钥匙旋转策略进行比较.
主要方法:
- 实施了强化学习 (RL) 模型,用于自适应键旋转.
- 在模拟的Zigbee网络环境中测试了RL模型.
- 在30天内使用诸如网络效率,DDoS响应,弹性,延迟和数据包丢失等指标评估性能.
主要成果:
- 该RL模型显著优于传统方法 (周期性,异常检测,启发式,静态).
- 证明了网络效率的提高,更高的入侵检测率,以及对DDoS攻击的更快的响应时间.
- 在各种模拟攻击和波动的流量下展示了卓越的资源管理和网络弹性.
结论:
- 由人工智能驱动的自适应策略,特别是RL,为增强Zigbee网络安全提供了强大的解决方案.
- 拟议的RL模型为物联网环境中的钥匙管理提供了更智能和更有效的方法.
- 这项研究为在不断变化的物联网环境中实现更安全,更有弹性的无线通信铺平了道路.
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