| Title | Machine learning-based IP Camera identification system |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Huang, Cheng-Wei, Wu, Tien-Yi, Tai, Yuan, Shao, Ching-Hsuan, Chen, Lo-An, Tsai, Meng-Hsun |
| Conference Name | 2020 International Computer Symposium (ICS) |
| Date Published | Dec. 2020 |
| Publisher | IEEE |
| ISBN Number | 978-1-7281-9255-0 |
| Keywords | Collaboration, composability, Information security, Internet, ip privacy, machine learning, Object recognition, policy-based governance, privacy, pubcrawl, resilience, Resiliency, Software, surveillance |
| Abstract | With the development of technology, application of the Internet in daily life is increasing, making our connection with the Internet closer. However, with the improvement of convenience, information security has become more and more important. How to ensure information security in a convenient living environment is a question worth discussing. For instance, the widespread deployment of IP-cameras has made great progress in terms of convenience. On the contrary, it increases the risk of privacy exposure. Poorly designed surveillance devices may be implanted with suspicious software, which might be a thorny issue to human life. To effectively identify vulnerable devices, we design an SDN-based identification system that uses machine learning technology to identify brands and probable model types by identifying packet features. The identifying results make it possible for further vulnerability analysis. |
| URL | https://ieeexplore.ieee.org/document/9359032 |
| DOI | 10.1109/ICS51289.2020.00090 |
| Citation Key | huang_machine_2020 |
Machine learning-based IP Camera identification system