# ZigBeeNet: Decrypted Zigbee IoT Network Traffic Dataset in Smart Home Environment
###### Abstract  
The number of smart homes is increasing steadily. One of the first technologies that comes to mind when talking about smart homes is Zigbee, which stands out for its low cost, low latency, low power consumption, and mesh networking capabilities. One of the key features of Zigbee is the encryption of payloads within its frames for security purposes. However, being able to decrypt this payload is crucial for fully understanding its operation and for purposes such as testing the network's security. Therefore, in this paper, we present the decrypted Zigbee IoT Network Traffic dataset, ZigBeeNet. We captured packets using Wireshark in real time from a smart home with 15 Zigbee devices over 20 days and saved them in pcap files. Additionally, we used a key extraction method to obtain the network key, decrypt the payload data, and analyze the characteristic features of network traffic, which we present in this paper. ZigBeeNet will be useful in wider areas than existing datasets with its ability to support network security research, pattern analysis, network performance analysis, and Zigbee traffic generator. We believe that this open-source dataset will contribute significantly to a wide range of industrial and academic research applications. Zigbee; smart home; IoT; network traffic; decrypted dataset; key extraction; network key; network traffic characteristics +
Footnote †: journal: Applied sciences  
payload, Zigbee prevents unauthorized access to sensitive information, such as device commands or sensor readings. However, for analytical purposes, being able to decrypt these data is essential for understanding the full scope of the network's operation. The ability to decrypt the Zigbee frames offers several significant advantages. Decrypted data can be used for many different purposes. The data can be used to identify the device's status [6], examine system operation, detect errors, and conduct performance analysis. This helps to analyze user behavior and habits in smart homes. For example, the brightness of the lights in a smart home or the temperature setting of the thermostat. Analytical modeling, simulation, and actual deployment are the most common methods used to analyze the performance of smart home communication systems. However, decrypted data from Zigbee devices opens new possibilities for enhancing this analysis. By leveraging such data, machine learning models can be trained to make more accurate predictions about the system's behavior, helping to optimize device interactions and improve overall efficiency. In addition, for security reasons, decrypted data can be used to understand how an attack occurs and then take action. For this reason, it is critical to detect and fix security vulnerabilities and make systems more secure. In this paper, we focused on creating a comprehensive dataset representing traffic in a Zigbee network. Zigbee communication, like most wireless protocols, employs encryption to secure transmitted data, which presents challenges in accessing and analyzing the detailed structure of network traffic. In most papers, the prepared or used dataset does not contain a decrypted payload due to not having extracted the network key. In our case, we were able to decrypt not only the transmitted traffic but also the payload of individual messages by using the link key and capturing the network key. This level of insight allows for an accurate representation of Zigbee's communication flows. The primary aim of this research is to investigate Zigbee network traffic characteristics. Since Zigbee is a protocol used for low-power and low-data-rate WPANs, researching Zigbee network traffic characteristics is important for security, privacy, efficiency, power consumption, and network performance. Also, understanding the characteristics of Zigbee network traffic is essential, especially in distributed systems such as large-scale networks and IoT.