An Enhanced Electrocardiogram Biometric Authentication System Using Machine Learning via Cloud of Things

  • Ebrahim Al Zaabi

Student thesis: Doctoral Thesis


Modern healthcare services cater to the needs of patients by embracing new technologies such as wearable devices and the cloud of things (CoT). This new technology paradigm provides more facilities and enhancements to existing healthcare services by allowing greater flexibility in terms of monitoring patients remotely and transmitting data to central servers. However, many cybersecurity issues must be considered when we introduce wearable devices to the healthcare sector, such as privacy and data security. Some of these cybersecurity issues have been addressed by other researchers, but they mainly considered the security of individual devices without the CoT. Their solutions, therefore, remain vulnerable to attacks such as man-in-the-middle, masquerading, modification, eavesdropping, repudiation, replay, and denial of service. We propose a new healthcare system that combines device-level and CoT security to provide robustness against the attacks listed above. The Scyther verification tool was used to verify the robustness of the proposed system. Furthermore, traditional authentication systems use passwords or token-based techniques that require 'something you know, and something you have.' The disadvantages of these systems include the potential risks of forgetfulness, loss, and theft. To address these shortcomings, biometric authentication is rapidly replacing traditional methods and is becoming a part of everyday life. The electrocardiogram (ECG) is one of the most recent traits considered for biometric techniques. This thesis consists of two main parts, which consider the cybersecurity of healthcare systems via the CoT, and ECG-based biometric authentication using machine learning techniques. An ECG-based biometric authentication system is suitable for security checks and hospital environments.
Date of AwardOct 2019
Original languageAmerican English


  • Internet of Things (IoT)
  • Cloud of Things (CoT)
  • Biomedical signal processing
  • Electrocardiogram (ECG)
  • Machine learning (ML)
  • Decision Tree (DT)
  • multivariable regression.

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