| Title |
Intrusion Detection in Smart City Hospitals using Ensemble Classifiers |
| ID_Doc |
45612 |
| Authors |
Saba, T |
| Title |
Intrusion Detection in Smart City Hospitals using Ensemble Classifiers |
| Year |
2020 |
| Published |
|
| DOI |
10.1109/DeSE51703.2020.9450247 |
| Abstract |
The idea of Internet of Medical Things (IOMT) is used as health intelligence in the smart hospital to assist medical staff for diagnosis and patient care. Smart hospitals play an essential role in digitizing healthcare facilities that could enhance a smart city project's scalability and efficiency. However, in the smart healthcare environment, IoMT devices face high vulnerability. Cyber-security is an essential aspect of a smart city that could achieve a secure environment for smart healthcare. Thus, the Intrusion Detection System (IDS) is used as a protection layer of communication towards cybersecurity for the latest devices and networks systems. In this paper, principal component analysis (PCA) is used for feature reduction and ensemble-based classifiers are used to predict intrusion attacks on the networks. KDDCup'99' dataset has been employed and performance is evaluated in terms of accuracy, precision, recall and F-score. |
| Author Keywords |
Intrusion detection; IoMT; Smart city; Ensemble classifiers; Healthcare; Health systems |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Conference Proceedings Citation Index - Science (CPCI-S) |
| EID |
WOS:000687851400063 |
| WoS Category |
Computer Science, Information Systems; Computer Science, Interdisciplinary Applications |
| Research Area |
Computer Science |
| PDF |
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