基于机器学习的ZigBee网络异常检测

Tomoya Oshio, Satoshi Okada, Takuho Mitsunaga
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引用次数: 4

摘要

随着信息技术的发展,物联网设备正在迅速普及。ZigBee是物联网设备中使用的短程无线通信标准之一,虽然通信速度较慢,但由于其低功耗和低成本运营,预计将用于智能家居和工业控制系统。但是,与有线通信相比,ZigBee更容易对无线通信进行窃听和发送伪造的数据包,因此有可能受到网络攻击。为了在智能家居和工业控制系统中安全使用ZigBee,有必要开发一种快速检测网络攻击的方法。本文提出了一种基于机器学习的Zigbee网络异常检测系统。我们关注ZigBee通信的特点,并研究一种使用机器学习检测ZigBee网络异常和网络攻击的方法。此外,由于我们主要强调实用性,我们提出的系统是简单的,包括广泛使用的工具,如Wireshark。为了评估我们提出的系统的检测精度,我们进行了一些实验。实验结果表明,该系统能够以较高的准确率检测攻击。此外,我们改变了机器学习中使用的特征,并讨论了哪些特征对异常检测有高贡献。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Machine Learning-based Anomaly Detection in ZigBee Networks
With the development of information technology, IoT devices are spreading rapidly. ZigBee is one of the short-range wireless communication standards used in IoT devices and is expected to be used in smart homes and industrial control systems because of its low power consumption and low-cost operation despite its low communication speed. However, ZigBee can be subject to cyber-attacks because eavesdropping on packets and sending forged packets against wireless communication is easier than wired ones. In order to use ZigBee safely in smart home and industrial control systems, it is necessary to develop a method to detect cyber-attacks quickly. In this paper, we propose a machine learning-based anomaly detection system for Zigbee networks. We focus on characteristics of ZigBee communication and investigate a method to detect network anomalies and cyber attacks on ZigBee networks using machine learning. Furthermore, since we primarily put emphasis on practicality, our proposed system is simple and consists of widely used tools such as Wireshark. To evaluate the detection accuracy of our proposed system, we conduct some experiments. As a result, it is shown that our proposed system can detect attacks with high accuracy. In addition, we varied the features used in machine learning and discuss which feature has a high contribution to anomaly detection.
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