| Title |
Machine Learning based Access Control Framework for the Internet of Things |
| ID_Doc |
41049 |
| Authors |
Outchakoucht, A; Abou El Kalam, A; Es-Samaali, H; Benhadou, S |
| Title |
Machine Learning based Access Control Framework for the Internet of Things |
| Year |
2020 |
| Published |
International Journal Of Advanced Computer Science And Applications, 11, 2 |
| DOI |
|
| Abstract |
The main challenge facing the Internet of Things (IoT) in general, and IoT security in particular, is that humans have never handled such a huge amount of nodes and quantity of data. Fortunately, it turns out that Machine Learning (ML) systems are very effective in the presence of these two elements. However, can IoT devices support ML techniques? In this paper, we investigated this issue and proposed a twofold contribution: a thorough study of the IoT paradigm and its intersections with ML from a security perspective; then, we actually proposed a holistic ML-based framework for access control, which is the defense head of recent IT systems. In addition to learning techniques, this second pillar was based on the organization and attribute concepts to avoid role explosion problems and applied to a smart city case study to prove its effectiveness. |
| Author Keywords |
Access control; internet of things; machine learning; security; smart city |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Emerging Sources Citation Index (ESCI) |
| EID |
WOS:000518468600043 |
| WoS Category |
Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
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