# Dual-IoTID: A Session-Based Dual IoT Device Identification Model
## 6 Discussions and Conclusions  
Identification of IoT device types is an essential part of recognizing instances of IoT devices, which contributes to establishing robust device authentication. In this study, we introduced a novel dual-machine learning model named Dual-IoTID, designed for IoT device identification. Unlike previous works that focused on packet headers and network layer features, our approach concentrated on the network communication payload, utilizing payload content and communication features for IoT device identification.  
Specifically, we commenced by categorizing IoT device sessions according to their destination ports, subsequently extracting frequent items according to aggregation results. Following this, the sessions were converted into a word vector matrix, which served as the input for the first-layer classifier, generating an initial classification confidence matrix. Ultimately, this confidence matrix, coupled with the extracted session communication features, was fed into the second-layer classifier to train the definitive classifier.  
Experimental results demonstrate the outstanding accuracy of Dual-IoTID. Furthermore, our method accurately identified devices of different types from the same vendor. Even in Internet environments, Dual-IoT demonstrated an excellent identification performance. Due to our method's focus solely on session payloads, irrespective of the protocols used, it can accurately identify devices even when employing encrypted communication, thus enhancing its versatility. Similarly, Dual-IoTID is applicable in network environments utilizing NAT, owing to the same reasons.  
Dual-IoTID offers a practical and efficient solution for IoT device identification. While the Dual-IoTID algorithm represents a significant stride in enhancing the accuracy of IoT device identification, it is not without its limitations. For instance, despite being designed with the intent of bolstering network security, the in-depth analysis of session payloads may inadvertently encroach upon user privacy, posing ethical and legal concerns. Moving forward, our objective is to extend the application of this method to broader scenarios, including new areas such as agriculture, industry, healthcare, and smart cities, and to further refine IoT device risk detection capabilities from this foundation. Concurrently, we intend to optimize the method to facilitate the identification of IoT devices in real-time scenarios. Ultimately, we aim to develop advanced privacy-preserving technologies that safeguard sensitive IoT data while enabling meaningful risk assessments. In an increasingly interconnected world, striking a balance between privacy and security is paramount. We aspire to establish a more comprehensive and dynamic framework for IoT device risk detection, fostering a safer and more resilient IoT eco-system.  
Conceptualization, T.Z. and T.H.; methodology, T.Z. and K.Y.; software, T.Z.; validation, T.Z., K.Y. and F.L.; data curation, T.Z. and Y.C.; writing--original draft preparation, T.Z.; writing--review and editing, M.Y. and T.H.; funding acquisition, T.H. All authors have read and agreed to the published version of the manuscript.  
The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was supported by the Presidential Foundation of CAEP (Grant No. YZJJZQ2023026).  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.