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Classifying brain signals collected by wearable Internet of Things (IoT) sensors, especially brain-computer interfaces (BCIs), is one of the fastest-growing areas of research. However, research has mostly ignored the secure storage and…

The rapid expansion of the Internet of Things (IoT) ecosystem has transformed various sectors but has also introduced significant cybersecurity challenges. Traditional centralized security methods often struggle to balance privacy…

This article aims to study intrusion attacks and then develop a novel cyberattack detection framework to detect cyberattacks at the network layer (e.g., Brute Password and Flooding of Transactions) of blockchain networks. Specifically, we…

Integrating blockchain technology with the Internet of Things offers transformative possibilities for enhancing network security and privacy in the contemporary digital landscape, where interconnected devices and expansive networks are…

This study proposes a framework to enhance privacy in Blockchain-based Internet of Things (BIoT) systems used in the healthcare sector. The framework addresses the challenge of leveraging health data for analytics while protecting patient…

The rapid adoption of Internet of Things (IoT) devices in healthcare has introduced new challenges in preserving data privacy, security and patient safety. Traditional approaches need to ensure security and privacy while maintaining…

Medical health care centers are envisioned as a promising paradigm to handle the massive volume of data of COVID-19 patients using artificial intelligence (AI). Traditionally, AI techniques often require centralized data collection and…

For the modern world where data is becoming one of the most valuable assets, robust data privacy policies rooted in the fundamental infrastructure of networks and applications are becoming an even bigger necessity to secure sensitive user…

This study proposes an advanced Federated Learning (FL) framework designed to enhance data privacy and security in IoT environments by integrating Decentralized Attribute-Based Encryption (DABE), Homomorphic Encryption (HE), Secure…

The increasing deployment of Internet-of-Things (IoT) devices has accelerated the use of distributed learning frameworks, where data remains local while model updates are shared across decentralized systems. Although this reduces…

Blockchain transactions have gained widespread adoption across various industries, largely attributable to their unparalleled transparency and robust security features. Nevertheless, this technique introduces various privacy concerns,…

The continuous strengthening of the security posture of IoT ecosystems is vital due to the increasing number of interconnected devices and the volume of sensitive data shared. The utilisation of Machine Learning (ML) capabilities in the…

In the light of the growing connectivity and sensitivity of industrial data, cyberattacks and data breaches are becoming more common in the Industrial Internet of Things (IIoT). To cope with such threats, this study presents an anomaly…

The integration of blockchain technology in Internet of Things (IoT) environments is a revolutionary step towards ensuring robust security and enhanced privacy. This paper delves into the unique challenges and solutions associated with…

We introduce a deep learning framework able to deal with strong privacy constraints. Based on collaborative learning, differential privacy and homomorphic encryption, the proposed approach advances state-of-the-art of private deep learning…

The application of machine learning (ML) algorithms are massively scaling-up due to rapid digitization and emergence of new tecnologies like Internet of Things (IoT). In today's digital era, we can find ML algorithms being applied in the…

Industrial Internet of Things (IIoT) systems have become integral to smart manufacturing, yet their growing connectivity has also exposed them to significant cybersecurity threats. Traditional intrusion detection systems (IDS) often rely on…

Federated learning is a distributed mechanism that trained large-scale neural network models with the participation of multiple clients and data remains on their devices, only sharing the local model updates. With this feature, federated…

Edge computing-based Next-Generation Wireless Networks (NGWN)-IoT offer enhanced bandwidth capacity for large-scale service provisioning but remain vulnerable to evolving cyber threats. Existing intrusion detection and prevention methods…

Security is one of the fundamental challenges in the Internet of Things (IoT) due to the heterogeneity and resource constraints of the IoT devices. Device classification methods are employed to enhance the security of IoT by detecting…