Driven by the rapid escalation of its utilization, as well as ramping commercialization, Internet of Things (IoT) devices increasingly face security threats. Apart from denial of service, privacy, and safety concerns, compromised devices can be used as enablers for committing a variety of crime and e-crime. Despite ongoing research and study, there remains a significant gap in the thorough analysis of security challenges, feasible solutions, and open secure problems for IoT. To bridge this gap, we provide a comprehensive overview of the state of the art in IoT security with a critical investigation-based approach. This includes a detailed analysis of vulnerabilities in IoT-based systems and potential attacks. We present a holistic review of the security properties required to be adopted by IoT devices, applications, and services to mitigate IoT vulnerabilities and, thus, successful attacks. Moreover, we identify challenges to the design of security protocols for IoT systems in which constituent devices vary markedly in capability (such as storage, computation speed, hardware architecture, and communication interfaces). Next, we review existing research and feasible solutions for IoT security. We highlight a set of open problems not yet addressed among existing security solutions. We provide a set of new perspectives for future research on such issues including secure service discovery, on-device credential security, and network anomaly detection. We also provide directions for designing a forensic investigation framework for IoT infrastructures to inspect relevant criminal cases, execute a cyber forensic process, and determine the facts about a given incident. This framework offers a means to better capture information on successful attacks as part of a feedback mechanism to thwart future vulnerabilities and threats. This systematic holistic review will both inform on current challenges in IoT security and ideally motivate their future resolution.

Blockchain technology is emerging as a pivotal framework to enhance the security of internet-based systems, especially as advancements in machine learning (ML), artificial intelligence (AI), and cyber–physical systems such as smart grids and IoT applications in healthcare continue to accelerate. Although these innovations promise significant improvements, security remains a critical challenge. Blockchain offers a secure foundation for integrating diverse technologies; however, vulnerabilities—including adversarial exploits—can undermine performance and compromise application reliability. To address these risks effectively, it is essential to comprehensively analyze the vulnerability landscape of blockchain systems. This paper contributes in two key ways. First, it presents a unique layer-based framework for analyzing and illustrating security attacks within blockchain architectures. Second, it introduces a novel taxonomy that classifies existing research on blockchain vulnerability detection. Our analysis reveals that while ML and deep learning offer promising approaches for detecting vulnerabilities, their effectiveness often depends on access to extensive and high-quality datasets. Additionally, the layer-based framework demonstrates that vulnerabilities span all layers of a blockchain system, with attacks frequently targeting the consensus process, network integrity, and smart contract code. Overall, this paper provides a comprehensive overview of blockchain security threats and detection methods, emphasizing the need for a multifaceted approach to safeguard these evolving systems.

A wide range of applications of the Internet of Things (IoT) is anticipated in the forthcoming hyper-connected society, including in classroom-based learning. To support flexible and resource-efficient learning environments, this paper proposes ContEd, a Container-based Framework for Flexible IoT Classrooms. ContEd enables the design and deployment of lightweight, isolated, and course-specific containers, offering the flexibility of Virtual Machine (VM)-based systems while significantly reducing setup and maintenance costs. The framework is particularly well-suited for resource-constrained devices and lightweight operating systems typical of IoT-enabled classrooms. To approach optimal performance, the ContEd architecture incorporates user type, urgency, and resource-usage-based course classification when creating containers, ensuring that each course receives a tailored environment. The framework integrates a role-based access control mechanism to prevent unauthorized or malicious content installation within containers. To facilitate automatic deployment of IoT-enabled classrooms, ContEd includes resource allocation strategies for device placement, container scheduling, and migration, enabling efficient management of limited resources. Experimental results demonstrate that ContEd outperforms traditional VM-based classroom models in terms of cost, flexibility, and efficiency, particularly in resource-constrained scenarios, validating its suitability for modern IoT-enabled learning environments.