Data augmentation for attack detection on IoT Telehealth Systems
| dc.contributor.author | Khan, Zaid A. | |
| dc.contributor.supervisor | Gebali, Fayez | |
| dc.contributor.supervisor | El-Kharashi, Mohamed Watheq | |
| dc.date.accessioned | 2022-03-12T00:56:14Z | |
| dc.date.available | 2022-03-12T00:56:14Z | |
| dc.date.copyright | 2022 | en_US |
| dc.date.issued | 2022-03-11 | |
| dc.degree.department | Department of Electrical and Computer Engineering | en_US |
| dc.degree.level | Master of Applied Science M.A.Sc. | en_US |
| dc.description.abstract | Telehealth is an online health care system that is extensively used in the current pandemic situation. Our proposed technique is considered a fog computing-based attack detection architecture to protect IoT Telehealth Networks. As for IoT Telehealth Networks, the sensor/actuator edge devices are considered the weakest link in the IoT system and are obvious targets of attacks such as botnet attacks. In this thesis, we introduce a novel framework that employs several machine learning and data analysis techniques to detect those attacks. We evaluate the effectiveness of the proposed framework using two publicly available datasets from real-world scenarios. These datasets contain a variety of attacks with different characteristics. The robustness of the proposed framework and its ability, to detect and distinguish between the existing IoT attacks that are tested by combining the two datasets for cross-evaluation. This combination is based on a novel technique for generating supplementary data instances, which employs GAN (generative adversarial networks) for data augmentation and to ensure that the number of samples and features are balanced. | en_US |
| dc.description.scholarlevel | Graduate | en_US |
| dc.identifier.uri | http://hdl.handle.net/1828/13798 | |
| dc.language | English | eng |
| dc.language.iso | en | en_US |
| dc.rights | Available to the World Wide Web | en_US |
| dc.subject | Internet of Things (IoT) | en_US |
| dc.subject | Machine Learning | en_US |
| dc.subject | Generative Adversarial Networks (GAN) | en_US |
| dc.subject | Intrusion detection systems (IDSs) | en_US |
| dc.subject | Cross dataset | en_US |
| dc.subject | Security | en_US |
| dc.title | Data augmentation for attack detection on IoT Telehealth Systems | en_US |
| dc.type | Thesis | en_US |