| Form of studies |
Master |
| Title of the study programm |
Cybersecurity Engineering |
| Title in original language |
Lietu interneta drošības izaicinājumu analīze veselības aprūpes sistēmā - pārskats un risku mazināšana |
| Title in English |
Analysis of IoT Security Challenges in Healthcare System - a Review and Risk Mitigation |
| Department |
Faculty Of Computer Science Information Tehnology And Energy |
| Scientific advisor |
Antons Patļins |
| Reviewer |
Oksana Ņikiforova |
| Abstract |
The wireless communication technology is IOT (internet of things) where the physical device is connecting to internet for communication. In recent years IOT technology is rapidly growing in all sectors such as Healthcare system, transportation system and smart city. In healthcare system, the IOT (internet of things) plays the major role. IOT technology is used from patients’ wearable devices to monitoring system in all healthcare devices. The usage of IOT devices is completely changing the human existence. The security of IOT devices is an important issue these days, specifically in healthcare environment. Destroying the IOT vulnerabilities and threats were lately disclosed by attackers. The traditional way of preventing IOT devices network security from malicious activity is well established but existing traditional network security mechanism is not applicable for IOT device because of its qualities of limitations. To provide security to the IOT device, modern methods like dataset or specific tools are required.
By observing all security challenges in IOT healthcare system, the author of the thesis has developed a framework using machine learning technique. The framework is enhanced version for detecting anomaly in healthcare system, it detects the malicious traffic coming from IOT based healthcare device. To test the framework, the existing datasets are used. The dataset is IOT based IOT healthcare environment which consist of nine patients monitoring devices and connected two beds. The dataset used IOT flock tool for devices. In proposed model, the machine learning algorithms called classifiers are used to detect the cyber-attack from dataset used of healthcare environment in purpose of predicting the result. furthermore, the performance analysis and predictive has been provided for insights of cyber-attacks in IOT healthcare system. The proposed framework will aid the development of environment aware IOT security solution, especially for vulnerable sector like an IOT healthcare system.
The current master thesis consists of 80 pages, including 23 tables, 17 figures and 13 equations. |
| Keywords |
Atslēgvārdi: IOT, Veselības aprūpes sistēma, Uzbrukumi, Mašīnmācība, kiberuzbrukumi. |
| Keywords in English |
IOT, Healthcare system, Attacks, Machine learning, cyber-attack. |
| Language |
eng |
| Year |
2024 |
| Date and time of uploading |
29.05.2024 20:32:13 |
