一个具有成本效益的自适应性修复策略,以减轻DDoS能力的物联网尸网络
1School of Big Data & Software Engineering, Chongqing University, Chongqing, China.
PloS one
|December 26, 2024
概括
本研究介绍了一种具有成本效益的自适应性修复策略 (ARS),用于打击物联网 (IoT) 尸网络发动分布式拒绝服务 (DDoS) 攻击. 开发的模型和算法有效地减轻物联网尸软件的传播,以提高网络安全性.
科学领域:
- 网络安全 网络安全
- 网络安全 网络安全
- 物联网 (IoT) 安全 安全 物联网
背景情况:
- 分布式拒绝服务 (DDoS) 攻击是一个重大威胁,通常从大型物联网 (IoT) 机器人网络发起.
- 物联网设备的资源限制和动态状态需要成本效益和适应性防御机制来抵御DDoS攻击.
研究的目的:
- 开发一个具有成本效益和适应性的修复策略 (ARS) 来缓解物联网尸网络的DDoS攻击.
- 建立一个物联网尸软件传播模型,考虑攻击和防御动态.
- 将ARS问题作为数据驱动的最佳控制挑战来解决.
主要方法:
- 建立了一个物联网尸软件传播模型,以捕捉攻击和防御下的网络状态演变.
- 该ARS问题是作为一个数据驱动的最佳控制问题,整合学习和从网络流量数据传播参数的预测.
- 基于最佳控制理论的代算法被提出来解决这个问题,并推导出时间变化的参数和修复策略.
主要成果:
- 该研究成功地学习了物联网机器人软件的时间变化的传播参数.
- 开发了一种新的,具有成本效益和适应性的维修策略,并通过数值获得.
- 计算机实验验证实了学习参数的性能和由此产生的修复策略.
结论:
- 提出的数据驱动的最佳控制方法有效地解决了开发物联网尸网络适应性修复策略的挑战.
- 开发的ARS为在资源有限的物联网环境中减轻DDoS攻击提供了有希望的解决方案.
- 这项研究有助于提高物联网网络对复杂网络威胁的弹性和安全性.
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