부산대학교 도서관

Recent growth in the utilization of IoT has offered convenience and utility, but has also increased security risk. Many devices lack the capacity to support adequate encryption or other common means of protection, and are often designed for easy connection out-of-the-box exposing vulnerabilities related to default configurations. Managing IoT devices in a network can be difficult as MAC addresses are easily spoofed, creating a need for techniques to properly identify and monitor membership. Many of the proposed solutions for IoT device identification require complex feature extraction and engineering. In addition, little work has been done to identify individual devices from among identical peers. We propose a novel hashing algorithm, FlexHash, and show that we are able to identify identical devices with a very high degree of accuracy using only a single packet of network traffic. By applying hybrid locality sensitive hashing in combination with machine learning our approach achieves accuracy scores as high as 98% for identical devices and 99% for identifying device genre.