Internet of Things (IoT) technology such as intelligent devices, sensors, actuators and wearables have been integrated in the healthcare industry, thus contributing in the creation of smart hospitals and remote assistance environments. Ensuring the eHealth network adopts the appropriate security measures in order to effectively protect sensitive patient data against malicious attempts is a tough challenge. Devices composing eHealth infrastructure are considered to be easily exploitable.To that end, a solution monitoring the intelligent healthcare environment is of essence. In addition, by digitalising all health records, appropriate measures need to be implemented in order for patient records to be accessible by authorized personnel only. Furthermore, creating interoperable systems, capable of being integrated by multiple organizations such as hospitals and insurance companies, while maintaining a General Data Protection Regulation-friendly posture, providing access to health data is a great importance for optimal patient assistance. To address both concerns, we present a framework featuring a multi-layer tool for providing a highly effective security solution specifically designed to address the eHealth requirements, and a blockchain access control component, based on smart contracts to provide access control for authorized users to patient records and health data in a distributed way.
Internet of Things (IoT) is a concept adopted in nearly every aspect of human life, leading to an explosive utilization of intelligent devices. Notably, such solutions are especially integrated in the industrial sector, to allow the remote monitoring and control of critical infrastructure. Such global integration of IoT solutions has led to an expanded attack surface against IoT-enabled infrastructures. Artificial intelligence and machine learning have demonstrated their ability to resolve issues that would have been impossible or difficult to address otherwise; thus, such solutions are closely associated with securing IoT. Classical collaborative and distributed machine learning approaches are known to compromise sensitive information. In our paper, we demonstrate the creation of a network flow-based Intrusion Detection System (IDS) aiming to protecting critical infrastructures, stemming from the pairing of two machine learning techniques, namely, federated learning and active learning. The former is utilized for privately training models in federation, while the latter is a semi-supervised approach applied for global model adaptation to each of the participant’s traffic. Experimental results indicate that global models perform significantly better for each participant, when locally personalized with just a few active learning queries. Specifically, we demonstrate how the accuracy increase can reach 7.07% in only 10 queries.
The highly beneficial contribution of intelligent systems in the industrial domain is undeniable. Automation, supervision, remote control, and fault reduction are some of the various advantages new technologies offer. A protocol demonstrating high utility in industrial settings, and specifically, in smart grids, is Distributed Network Protocol 3 (DNP3), a multi-tier, application layer protocol. Notably, multiple industrial protocols are not as securely designed as expected, considering the highly critical operations occurring in their application domain. In this paper, we explore the internal vulnerabilities-by-design of DNP3, and proceed with the implementation of the attacks discovered, demonstrated through 8 DNP3 attack scenarios. Finally, we design and demonstrate a Deep Neural Network (DNN)-based, multi-model Intrusion Detection Systems (IDS), trained with our experimental network flow cyberattack dataset, and compare our solution with multiple machine learning algorithms used for classification. Our solution demonstrates a high efficiency in the classification of DNP3 cyberattacks, showing an accuracy of 99.0%.
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