Security engineering with machine learning for adversarial resiliency in cyber physical systems
Security engineering with machine learning for adversarial resiliency in cyber physical systems
复制标题
通过机器学习进行安全工程，以提高网络物理系统的对抗弹性
DOI：
10.1117/12.2519372
复制
发表时间：
2019
期刊：
影响因子：
--
通讯作者：
Kamhoua, Charles
中科院分区：
文献类型：
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作者：
Olowononi, Felix;Rawat, Danda;Garuba, Moses;Kamhoua, Charles
最近的技术进步提供了在物理世界和网络空间之间架起桥梁的机会，这导致了复杂和多领域的网络物理系统(CP)，其中使用许多智能传感器和网络空间来监测和控制物理系统，以根据其运行环境实时做出反应。然而，CPS迅速采用智能、自适应和可远程访问的连接设备，使网络空间更加复杂和多样化，更容易受到大量网络攻击和对手的攻击。本文旨在设计、开发和评估一种分布式机器学习算法，以期为关键移动CP在对抗环境中提供安全保障。
Recent technological advances provide the opportunities to bridge the physical world with cyber-space that leads to complex and multi-domain cyber physical systems (CPS) where physical systems are monitored and controlled using numerous smart sensors and cyber space to respond in real-time based on their operating environment. However, the rapid adoption of smart, adaptive and remotely accessible connected devices in CPS makes the cyberspace more complex and diverse as well as more vulnerable to multitude of cyber-attacks and adversaries. In this paper, we aim to design, develop and evaluate a distributed machine learning algorithm for adversarial resiliency where developed algorithm is expected to provide security in adversarial environment for critical mobile CPS.