Lightweight Anomaly Detection Framework for IoT

Bianca Tagliaro Beasley, George D. O’Mahony, Sergi Gómez Quintana, A. Temko, E. Popovici
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引用次数: 1

Abstract

Internet of Things (IoT) security is growing in importance in many applications ranging from biomedical to environmental to industrial applications. Access to data is the primary target for many of these applications. Often IoT devices are an essential part of critical control systems that could affect well-being, safety, or inflict severe financial damage. No current solution addresses all security aspects. This is mainly due to the resource-constrained nature of IoT, cost, and power consumption. In this paper, we propose and analyse a framework for detecting anomalies on a low power IoT platform. By monitoring power consumption and by using machine learning techniques, we show that we can detect a large number and types of anomalies during the execution phase of an application running on the IoT. The proposed methodology is generic in nature, hence allowing for deployment in a myriad of scenarios.
物联网轻量级异常检测框架
从生物医学到环境再到工业应用,物联网(IoT)安全在许多应用中越来越重要。对数据的访问是许多这类应用程序的主要目标。通常,物联网设备是关键控制系统的重要组成部分,可能会影响健康、安全或造成严重的经济损失。目前还没有解决所有安全问题的解决方案。这主要是由于物联网的资源有限性、成本和功耗。在本文中,我们提出并分析了一种在低功耗物联网平台上检测异常的框架。通过监控功耗和使用机器学习技术,我们可以在物联网上运行的应用程序的执行阶段检测到大量和类型的异常。所建议的方法本质上是通用的,因此允许在无数的场景中部署。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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