| Title |
Machine Learning Based Security for Smart Cities |
| ID_Doc |
39259 |
| Authors |
Amaizu, GC; Lee, JM; Kim, DS |
| Title |
Machine Learning Based Security for Smart Cities |
| Year |
2022 |
| Published |
|
| DOI |
10.1109/APCC55198.2022.9943712 |
| Abstract |
The proliferation and wide usage of the Internet of Things (IoT) and related information and communication technologies (ICT) have led to the emergence of smart cities which comprises ubiquitous sensors, and heterogeneous network architectures. These cities are capable of relaying real-time information about the world which can then be used to improve the Qualify of Life (QoL). However, due to the unprecedented access to the city and personal data by smart city applications, there is an increase in both security and privacy threat. In this study, we propose a stacked generalization machine learning algorithm for the detection of cyberattacks in a smart city. The algorithm was tested using datasets from various smart city infrastructures. Simulation results show a high detection accuracy. |
| Author Keywords |
Artificial Intelligence; IoT; Machine Learning; Security; Smart City |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000918854200121 |
| WoS Category |
Engineering, Electrical & Electronic; Telecommunications |
| Research Area |
Engineering; Telecommunications |
| PDF |
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