Z-IoT: Passive Device-class Fingerprinting of ZigBee and Z-Wave IoT Devices
Leonardo Babun, Hidayet Aksu, Ryan Lucas, Kemal Akkaya, Elizabeth Serena Bentley, A. Selcuk Uluagac
Abstract
In addition to traditional networking devices (e.g., gateways, firewalls), current corporate and industrial networks integrate resource-limited Internet of Things (IoT) devices like smart outlets and smart sensors. In these settings, cyber attackers can bypass traditional security solutions and spoof legitimate IoT devices to gain illegal access to the systems. Thus, IoT device-class identification is crucial to protect critical networks from unauthorized access. In this paper, we propose Z-IoT, the first fingerprinting framework used to identify IoT device classes that utilize ZigBee and Z-Wave protocols. Z-IoT monitors idle network traffic among IoT devices to implement signature-based device-class fingerprinting mechanisms. Utilizing passive packet capturing techniques and optimal selection of filtering criteria and machine learning algorithms, Z-IoT identifies different types of IoT devices while guaranteeing the anonymity of the network data. To test Z-IoT's efficacy, we implemented several testbeds, including a total of 39 commodity IoT devices that communicate over ZigBee and Z-Wave protocols. Our experimental results showed an excellent performance in identifying different classes of IoT devices with average precision and recall of over 91%. Finally, the proposed framework yields no overhead to the IoT devices or the network traffic.