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Analysis of Network Traffic of Biomedical Devices Using Machine Learning

Parole chiave NETWORK TRAFFIC MACHINE LEARNING

Riferimenti MARCO MELLIA, MARTINO TREVISAN

Riferimenti esterni Idilio Drago (Prof. UniversitÓ di Torino)

Gruppi di ricerca SmartData@PoliTO

Tipo tesi SPERIMENTALE

Descrizione With e-Health technologies enabling remote treatments, security of health devices has become more important than ever. The scenario becomes even more alarming when considering threats to vital settings, with the health crisis that stepped up cyber-attacks on hospitals, healthcare, and medical research facilities. IoT is entering hospitals and healthcare by enabling remote patient assistance and monitoring. Expensive examination equipment and patient monitoring devices are nowadays connected to the hospital networks, possibly left exposed to the Internet. Patients' and doctors' devices are offered WiFi connectivity for both leisure and monitoring, with a BYOD policy that leaves attackers open field.

The goal of this thesis is to evaluate in practice the security of connected medical devices. The student will leverage network traffic analysis for that. Network traffic is a rich source of information and a powerful means to detect intrusion to ICT systems. Moreover, network packets may carry sensitive data in unencrypted form if the protocols are not correctly implemented or configured.

The student will analyze large datasets of network traffic from various sources (including traffic from biomedical devices) and use Artificial Intelligence and Machine Learning to identify potential risks for cybersecurity and privacy in a real hospital settings.


Scadenza validita proposta 04/10/2022      PROPONI LA TUA CANDIDATURA




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