Improving reliability in the internet of things through anomaly detection

Student thesis: Doctoral Thesis


IoT environments are increasingly being used to support critical infrastructure, placing an increased burden of responsibility on these systems to perform reliably and safely. IoT environments, however, are often laden with security and reliability issues relating to sensor performance and network security. This Thesis presents research towards engineering safer and more reliable IoT environments. Within this Thesis, a review of the state-of-the-art in reliability is performed to guide the solutions investigated. Part one of the Thesis concerns the area of sensor reliability. A study is performed to measure the impact of sensor failures in a smart environment. This study concluded with recommendations for sensor data preparation and model selection. Following this, a novel approach is presented for detecting anomalies in constrained sensors. This approach achieved an f-measure of 92.00% on a public activity recognition dataset. Part two of the Thesis considers the network layer of the IoT, and in particular the area of intrusion detection. A study is performed to develop a hierarchical intrusion detection system, which successfully lowers the number of false alarms recorded by the system to 0.59%. Next, multi-task learning is investigated for intrusion detection systems, and a model is proposed using balanced batch learning. This model achieves a macro average recall of 92.94%. This Thesis clearly demonstrated that solutions to improving reliability in the IoT are possible, at both a sensor and network level.
Date of AwardSept 2022
Original languageEnglish
SupervisorShuai Zhang (Supervisor), Christopher Nugent (Supervisor) & Ian Cleland (Supervisor)


  • Internet of things
  • Anomaly detection
  • Deep learning

Cite this