As an Internet of Things (IoT) technological key enabler, Wireless Sensor Networks (WSNs) are prone to different kinds of cyberattacks. WSNs have unique characteristics, and have several limitations which complicate the design of effective attack prevention and detection techniques. This paper aims to provide a comprehensive understanding of the fundamental principles underlying cybersecurity in WSNs. In addition to current and envisioned solutions that have been studied in detail, this review primarily focuses on state-of-the-art Machine Learning (ML) and Blockchain (BC) security techniques by studying and analyzing 164 up-to-date publications highlighting security aspect in WSNs. Then, the paper discusses integrating BC and ML towards developing a lightweight security framework that consists of two lines of defence, i.e, cyberattack detection and cyberattack prevention in WSNs, emphasizing the relevant design insights and challenges. The paper concludes by presenting a proposed integrated BC and ML solution highlighting potential BC and ML algorithms underpinning a less computationally demanding solution.
Recently, the process control network (PCN) of oil and gas installation has been subjected to amorphous cyber-attacks. Examples include the denial-of-service (DoS), distributed denial-of-service (DDoS), and man-in-the-middle (MitM) attacks, and this may have largely been caused b…
The rise in internet users has brought with it the impending threat of cybercrime as the Internet of Things (IoT) increases and the introduction of 5G technologies continues to transform our digital world. It is now essential to protect communication networks from illegal intrusi…
With the rapid advancements and notable achievements across various application domains, Machine Learning (ML) has become a vital element within the Internet of Things (IoT) ecosystem. Among these use cases is IoT security, where numerous systems are deployed to identify or thwar…
The Internet of Things (IoT) shares the idea of an autonomous system responsible for transforming physical computational devices into smart ones. Contrarily, storing and operating information and maintaining its confidentiality and security is a concerning issue in the IoT. Throu…
This paper introduces Optimal 5G Network Sub-Slicing Orchestration (ONSSO), a novel machine learning framework for dynamic and autonomous 5G network slice orchestration. The framework leverages the LazyPredict module to automatically select optimal supervised learning algorithms…
Emerging edge devices are transforming the Internet of Things (IoT) by enabling more responsive and efficient interactions between physical objects and digital networks. These devices support diverse applications, from health-monitoring wearables to environmental sensors, by movi…