الفهرس 
Chapter (1) IntroductionOnly 14 pages are availabe for public view
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Abstract
Recently, there has been considerable growth in the Internet of Things (IoT)-based healthcare applications; however, they suffer from a lack of Intrusion Detection Systems (IDS). Leveraging recent technologies, such as Machine Learning (ML), Deep Learning (DL), edge computing, and blockchain, can provide suitable and strong security solutions for preserving the privacy of medical data. In this thesis, FIDChain (Federated Intrusion Detection with Blockchain) IDS is proposed using lightweight Artificial Neural Network (ANN) in a Federated Learning (FL) way to ensure healthcare data privacy preservation. The blockchain technology provides a distributed ledger for aggregating the local weights and then broadcasting the updated global weights after averaging, that prevents poisoning attacks and provides data privacy preservation with negligible overhead. Applying the detection model at the edge protects the cloud if an attack happens, as it blocks the data from its gateway with smaller detection time and lesser computing and processing capacity as FL deals with smaller sets of data. The ANN and eXtreme Gradient Boosting (XGBoost) models were evaluated using the Botnets of Internet of Things dataset (Bot-IoT dataset). The results show that ANN models have higher accuracy and better performance with the heterogeneity of data in IoT devices, such as Intensive Care Unit (ICU) in healthcare systems. Testing the FIDChain with different datasets such as Communications Security Establishment & the Canadian Institute for Cybersecurity Intrusion Detection System dataset (CSE-CIC-IDS2018), Botnets Network in Internet of Things dataset (Bot Net IoT), and Knowledge Discovery and Data Mining Tools Competition dataset (KDD Cup 99). The test reveals that the Bot-IoT dataset has the most stable and accurate results for testing IoT applications, such as those used in healthcare systems.