Large-scale IoT Device Identification And Location

The Internet of Things has become an important part of smart cities,smart communities and other applications related to national economy and people's livelihood.In recent years,more and more IoT devices have access to network space,including web cameras,printers,routers,industrial control devices,and smart home appliances.The security issues of IoT devices have drawn the attention of industry and academia.Geographic location information for IoT devices and devices plays a key role in ensuring cyberspace security.However,the current commercial geographic location database can only provide coarse-grained location information,which can only be located at the national or city level,and cannot meet the needs of identifying and locating fine-grained IoT devices.This paper proposes a context-based IoT device identification and localization algorithm to improve the accuracy of device identification and positioning.In this paper,a fingerprint generation algorithm for IoT devices based on machine learning is implemented.Based on the application layer data message content,the paper extracts the text information and combines with the general machine learning algorithm to generate the fingerprint of IoT devices and improve the identification accuracy of IoT devices in the network space.Secondly,this paper proposes a measurement-based localization algorithm.Specifically,this paper extracts reliable geographical location information by naming entity recognition and regular expression,and generates a large amount of passive landmark information as the anchor node for measurement.Based on the Euclidean distance,this paper clusters the landmark nodes to obtain high-quality landmark information,and combines the delay information,network topology and routing information to calculate the latitude and longitude information of the target IoT device and improve its accuracy.Device positioning.In addition,this article graphically displays the location information of IoT devices by calling the Google Maps interface.In this paper,the feasibility of the algorithm is verified by building a prototype system.The experimental results show that the identification accuracy and coverage rate of the IoT devices reach 98%.On public data collection(nearly 100 million pieces of data information),IoT devices fingerprints found 5.52 million IoT devices.In terms of equipment positioning,this paper extracts 60,000 landmark information with latitude and longitude information,and the positioning accuracy is controlled within 100 kilometers.