The proliferation of inter-connected devices in critical industries, such as healthcare and power grid, is changing the perception of what constitutes critical infrastructure. The rising interconnectedness of new critical industries is driven by the growing demand for seamless access to information as the world becomes more mobile and connected and as the Internet of Things (IoT) grows. Critical industries are essential to the foundation of today’s society, and interruption of service in any of these sectors can reverberate through other sectors and even around the globe. In today’s hyper-connected world, the critical infrastructure is more vulnerable than ever to cyber threats, whether state sponsored, criminal groups or individuals. As the number of interconnected devices increases, the number of potential access points for hackers to disrupt critical infrastructure grows. This new attack surface emerges from fundamental changes in the critical infrastructure of organizations technology systems. This paper aims to improve understanding the challenges to secure future digital infrastructure while it is still evolving. After introducing the infrastructure generating big data, the functionality-based fog architecture is defined. In addition, a comprehensive review of security requirements in fog-enabled IoT systems is presented. Then, an in-depth analysis of the fog computing security challenges and big data privacy and trust concerns in relation to fog-enabled IoT are given. We also discuss blockchain as a key enabler to address many security related issues in IoT and consider closely the complementary interrelationships between blockchain and fog computing. In this context, this work formalizes the task of securing big data and its scope, provides a taxonomy to categories threats to fog-based IoT systems, presents a comprehensive comparison of state-of-the-art contributions in the field according to their security service and recommends promising research directions for future investigations.

The Internet of Things (IoT) is an emerging paradigm branded by heterogeneous technologies composed of smart ubiquitous objects that are seamlessly connected to the Internet. These objects are deployed as Low power and Lossy Networks (LLN) to provide innovative services in various application domains such as smart cities, smart health, and smart communities. The LLN is a form of a network where the interconnected devices are highly resource-constrained (i.e., power, memory, and processing) and characterized by high loss rates, low data rates, and instability in the communication links. Additionally, IoT devices produce a massive amount of confidential and security-sensitive data. Various cryptographic-based techniques exist that can effectively cope with security attacks but are not suitable for IoT as they incur high consumption of resources (i.e., memory, storage and processing). One way to address this problem is by offloading the additional security-related operations to a more resourceful entity such as a fog-based node. Generally, fog computing enables security and analysis of latency-sensitive data directly at the network’s edge. This paper proposes a novel Fog Security Service (FSS) to provide end-to-end security at the fog layer for IoT devices using two well-established cryptographic schemes, identity-based encryption, and identity-based signature. The FSS provides security services such as authentication, confidentiality, and non-repudiation. The proposed architecture would be implemented and evaluated in an OPNET simulator using a single network topology with different traffic loads. The FSS performed better when compared with the APaaS and the legacy method.

The Internet of Things (IoT) has revolutionized various domains, enabling interconnected devices to communicate and exchange data. The integration of Artificial Intelligence (AI) in IoT systems further enhances their capabilities and potential benefits. Unfortunately, in the era of AI, ensuring the privacy and security of the IoT faces novel and specific challenges. IoT security is imperative, necessitating comprehensive strategies, including comprehension of IoT security challenges, implementation of AI methodologies, adoption of resilient security frameworks, and handling of privacy and ethical concerns to construct dependable and secure IoT systems. It is vital to note that the term ’security’ encompasses a more comprehensive view than cyberattacks alone. Therefore, with an emphasis on securing against cyberattacks, this comprehensive survey also includes physical security threats on the IoT. It investigates the complexities and solutions for IoT systems, placing particular emphasis on AI-based security techniques. The paper undertakes a categorization of the challenges associated with ensuring IoT security, investigates the utilization of AI in IoT security, presents security frameworks and strategies, underscores privacy and ethical considerations, and provides insights derived from practical case studies. Furthermore, the survey sheds light on emerging trends concerning IoT security in the AI era. This survey provides significant contributions to the understanding of establishing dependable and secure IoT systems through an exhaustive examination of the present condition of IoT security and the ramifications of AI on it.

The Internet of Things (IoT) contains many devices that can compute and communicate, creating large networks. Industrial Internet of Things (IIoT) represents a developed application of IoT, connecting with embedded technologies in production in industrial operational settings to offer sophisticated automation and real-time decisions. Still, IIoT compels significant cybersecurity threats beyond jamming and spoofing, which could ruin the critical infrastructure. Developing a robust Intrusion Detection System (IDS) addresses the challenges and vulnerabilities present in these systems. Traditional IDS methods have achieved high detection accuracy but need improved scalability and privacy issues from large datasets. This paper proposes a Fog-enabled Federated Learning-based Intrusion Detection System (FFL-IDS) utilizing Convolutional Neural Network (CNN) that mitigates these limitations. This framework allows multiple parties in IIoT networks to train deep learning models with data privacy preserved and low-latency detection ensured using fog computing. The proposed FFL-IDS is validated on two datasets, namely the Edge-IIoTset, explicitly tailored to environments with IIoT, and CIC-IDS2017, comprising various network scenarios. On the Edge-IIoTset dataset, it achieved 93.4% accuracy, 91.6% recall, 88% precision, 87% F1 score, and 87% specificity for jamming and spoofing attacks. The system showed better robustness on the CIC-IDS2017 dataset, achieving 95.8% accuracy, 94.9% precision, 94% recall, 93% F1 score, and 93% specificity. These results establish the proposed framework as a scalable, privacy-preserving, high-performance solution for securing IIoT networks against sophisticated cyber threats across diverse environments.