| Project Reference: | ITP/043/23LP |
| Project Title: | Machine Learning Based Method for Wireless IoT Device Authentication and Detection in a Smart Environment |
| Hosting Institution: | LSCM R&D Centre (LSCM) |
| Abstract: | In 2020, the Hong Kong SAR government published the second edition of the Smart City Blueprint, putting forth 130 new initiatives aimed at utilizing innovations and technologies to deliver benefits and convenience to the public. Internet of Things (IoT) technology is at the core of smart cities. However, the adoption of such technology in physically open environments create larger attacking surfaces and open new vulnerabilities. A 2021 Deloitte article, Urban Future with a Purpose (available at https://www2.deloitte.com/us/en /insights/industry/public-sector/future-of-cities.html), lists cyber security as a key consideration for policy makers of smart cities and mentions that cities on average lose €2.8M/year from cyberattacks. To address this, implementing information security frameworks (e.g., ISO 27001:2022) is of paramount importance. However, it is difficult to comply with the proactive/continuous improvement requirements (e.g., under the plan-docheck- act principle) of these frameworks given the dynamic and physically open nature of a smart environment. We propose to develop and implement an efficient machine learning based method to perform physical layer authentication and detection on wireless IoT devices. This technology aims to facilitate proactive IoT device monitoring in a smart environment and help to provide a possible way to fulfil the aforementioned framework requirements. |
| Project Coordinator: | Dr Russell Siu Wai Yiu |
| Approved Funding Amount: | HK$ 2.79M |
| Project Period: | 1 Mar 2024 - 31 May 2025 |
