The market for Internet of Things (IoT) products and services has grown rapidly. It has been predicted that the deployment of these IoT applications will grow exponentially in the near future. However, the rapid growth of IoT brings new security risks and potentially opens up new types of attacks for systems and networks. This article outlines various techniques to carry out attacks on ZigBee-based IoT systems. We conducted penetration experiments on various possible attacks on Zigbee-based IoT. The purpose of this experiment’s results is for reference in developing an Intrusion Detection System (IDS) specifically for ZigBee-based IoT.
The market for Internet of Things (IoT) products and services has grown rapidly. It has been predicted that the deployment of these IoT applications will grow exponentially in the near future. However, the rapid growth of IoT brings new security risks and potentially opens new types of attacks for systems and networks. This article outlines various techniques for detecting known attacks in ZigBee-based IoT systems. We introduced an Detection System (IDS) specific for ZigBee using data analytics method, that are used to provide an accurate detection method for known attacks. This article looks at our IDS implementation covering a wide variety of detection techniques to detect known attacks ZigBee IoT systems.
This article outlines various techniques for detecting types of attacks that may arise in ZigBee-based IoT system. The researchers introduced a hybrid Intrusion Detection System (IDS), combining rule-based intrusion detection and machine learning-based anomaly detection. Rule-based attack detection techniques are used to provide an accurate detection method for known attacks. However, determining accurate detection rules requires significant human effort that is susceptible to error. If it is done incorrectly, it can result in false alarms. Therefore, to alleviate this potential problem, the system is being upgraded by combining it (hybrid) with machine learning-based anomaly detection. This article expounds the researchers’ IDS implementation covering a wide variety of detection techniques to detect both known attacks and potential new types of attacks in ZigBee-based IoT system. Furthermore, a safe and efficient meth-od for large-scale IDS data collection is introduced to provide a trusted reporting mechanism that can operate on the stringent IoT resource requirements appropriate to today's IoT systems.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.