Data Science and Cybersecurity Center, Howard University, Washington, DC 20059, USA.
Sensors (Basel). 2021 Feb 3;21(4):1034. doi: 10.3390/s21041034.
Machine learning (ML) has been emerging as a viable solution for intrusion detection systems (IDS) to secure IoT devices against different types of attacks. ML based IDS (ML-IDS) normally detect network traffic anomalies caused by known attacks as well as newly introduced attacks. Recent research focuses on the functionality metrics of ML techniques, depicting their prediction effectiveness, but overlooked their operational requirements. ML techniques are resource-demanding that require careful adaptation to fit the limited computing resources of a large sector of their operational platform, namely, embedded systems. In this paper, we propose cloud-based service architecture for managing ML models that best fit different IoT device operational configurations for security. An IoT device may benefit from such a service by offloading to the cloud heavy-weight activities such as feature selection, model building, training, and validation, thus reducing its IDS maintenance workload at the IoT device and get the security model back from the cloud as a service.
机器学习（ML）已成为一种可行的解决方案，可用于入侵检测系统（IDS），以保护物联网设备免受各种类型的攻击。基于机器学习的 IDS（ML-IDS）通常可以检测到由已知攻击以及新引入的攻击引起的网络流量异常。最近的研究重点是机器学习技术的功能指标，描述其预测有效性，但忽略了它们的操作要求。机器学习技术需要大量资源，需要进行仔细的调整以适应其操作平台的大部分计算资源，即嵌入式系统。在本文中，我们提出了一种基于云的服务架构，用于管理最适合不同物联网设备操作配置的 ML 模型，以实现安全性。物联网设备可以通过将特征选择、模型构建、训练和验证等繁重活动卸载到云端，从而从该服务中受益，从而减少物联网设备上的 IDS 维护工作量，并从云端获得安全模型作为服务。
