The widespread use of the Internet and the rapid growth of edge devices have increased the cyber-attack surface for these devices in Information and Communication Technologies (ICT). Attackers target edge devices due to their widespread use as essential components of daily life, driven by the Internet of Things (IoT) revolution and the 6G ecosystem. Protecting these devices from cyber attacks is crucial, given their limited storage, processing capabilities, and battery resources. Additional challenges include diversity, the swift development of numerous devices, and hardware trust issues. Existing security solutions are ill-suited for the resource-constrained environments (RCE) of IoT and 6G. This article conducts a comprehensive analysis of the applicability of cyber attacks in RCE and identifies attacks across four layers of the IoT protocol stack. This study reveals that 82% of application-layer attacks can be detected through network traffic analysis. Secondly, the technical requirements for trust architectures, models, modules, parameters, key technologies, and applications are presented. Third, a review of recently proposed trust management solutions, considering trust modules, is presented. In this context, 95% of the schemes address trust composition, while 58% focus on the trust propagation module, highlighting the need for effective trust management solutions to address these challenges. Additionally, this article analyzes the principles of Zero-Trust Architecture (ZTA) for resource-constrained devices. Furthermore, future research directions and open challenges for designing such solutions are also identified.

Due to the growth of Internet of Things (IoT) devices on networks with limited resources, developing a safe and effective authentication method for Device-to-Device (D2D) communication has become necessary. Conventional authentication methods frequently have high communication and processing overhead, which makes them inappropriate for resource-constrained IoT networks. An innovative, lightweight, and verifiably secure D2D authentication protocol designed for resource-constrained IoT networks is proposed in this research. The protocol minimizes energy and computational costs while ensuring strong security by utilizing mutual authentication and effective cryptographic primitives. According to security analysis, the proposed protocol resists widespread attacks, such as DoS, replay, man-in-the-middle, and impersonation attacks. Additionally, the accuracy and security features of the protocol are confirmed by formal verification using commonly used techniques such as the ROR model and ProVerif validation. It is a feasible option for IoT deployments as performance examinations show considerable improvements over current systems regarding energy consumption, communication, computation, storage, and bandwidth utilization.

Despite the many outstanding benefits of cloud computing, such as flexibility, accessibility, efficiency, and cost savings, it still suffers from potential data loss, security concerns, limited control, and availability issues. The experts introduced the edge computing paradigm to perform better than cloud computing for the mentioned issues and challenges because it is directly connected to the Internet-of-Things (IoT), sensors, and wearables in a decentralized manner to distribute processing power closer to the data source, rather than relying on a central cloud server to handle all computations; this allows for faster data processing and reduced latency by processing data locally at the ‘edge’ of the network where it’s generated. However, due to the resource-constrained nature of IoT, sensors, or wearable devices, the edge computing paradigm endured numerous data breaches due to sensitive data proximity, physical tampering vulnerabilities, and privacy concerns related to user-near data collection, and challenges in managing security across a large number of edge devices. Existing authentication schemes didn’t fulfill the security needs of the edge computing paradigm; they either have design flaws, are susceptible to various known threats—such as impersonation, insider attacks, denial of service (DoS), and replay attacks—or experience inadequate performance due to reliance on resource-intensive cryptographic algorithms, like modular exponentiations. Given the pressing need for robust security mechanisms in such a dynamic and vulnerable edge-IoT ecosystem, this article proposes an ECC-based robust authentication scheme for such a resource-constrained IoT to address all known vulnerabilities and counter each identified threat. The proof of correctness of the proposed protocol has been scrutinized through a well-known and widely used Real-Or-Random (RoR) model, ProVerif validation, and attacks’ discussion, demonstrating the thoroughness of the proposed protocol. The performance metrics have been measured by considering computational time complexity, communication cost, and storage overheads, further reinforcing the confidence in the proposed solution. The comparative analysis results demonstrated that the proposed ECC-based authentication protocol is 90.05% better in terms of computation cost, 62.41% communication cost, and consumes 67.42% less energy compared to state-of-the-art schemes. Therefore, the proposed protocol can be recommended for practical implementation in the real-world edge-IoT ecosystem.

In modern edge IoT-assisted e-healthcare, biosensors are implanted within the human body to collect real-time physiological data. These vast amounts of sensitive data are then transmitted to a physician's device for diagnosis and real-time analysis via an edge server. The data transmission over an open network channel makes it susceptible to numerous vulnerabilities, including side-channel and impersonation attacks. Existing research has highlighted inefficiencies that deter extensive cryptographic operations required to ensure data security. It faces difficulties with side-channel attacks, impersonation attacks, or struggles with efficiency due to demanding lightweight cryptographic requirements. This article presents an efficient and lightweight authentication scheme using biometric fuzzy extractors, incorporating an edge server to enhance security within the edge IoT e-healthcare paradigm. The scheme's security and correctness have been rigorously and comprehensively analyzed using the well-regarded random oracle model (ROM) and ProVerif, along with a comprehensive assessment of known vulnerabilities. Experimental results and comparative analysis based on communication, computation costs, energy consumption, latency, runtime, and scalability demonstrate that the proposed protocol outperforms existing solutions.

Past studies have proposed machine learning-based solutions for classifying software requirements, helping analysts, software architects, and developers efficiently and effectively identify the types of requirements. A common step performed by these solutions is to represent requirements as meaningful vectors. These vectors are used to identify and classify software requirements. The quality of the vector representations of software requirements determines the effectiveness, efficiency, and performance of the proposed solution. This study introduced a domain-specific pretrained model, Req2Vec, that extracts a distributed representation of non-functional software requirements (NFRs). Req2Vec is an unsupervised, domain-specific pre-trained model trained on thirty million samples of functional and non-functional requirements (FR and NFR). Req2Vec is aware of various requirements, including both functional and non-functional requirements, and seamlessly integrates them to learn representations of requirements. Using a limited supervised dataset, Req2Vec was investigated for various requirement classification tasks, including binary classification, reduced sets of NFR classifications, and all NFR classifications. In binary classification, the Req2Vec model achieved precision, recall, and F1 scores of 0.91, 0.87, and 0.89, respectively. For the reduced set of NFR classification, the average scores are 0.89, 0.85, and 0.87. For all classes, the average scores are 0.83, 0.77, and 0.80. A comparative analysis revealed that Req2Vec yields promising results in advancing technologies, demonstrating its effectiveness in accurately classifying NFRs.