The Internet of Vehicles (IoVs) integrates vehicles to the enormous realm of cyberspace which introduces some intelligence and convenience in the transportation sector. However, real-time traffic related information is exchanged over the open public internet among the vehicles and with other infrastructures. This exposes these networks to a myriad of security threats that can lead to accidents and congestions. Although many solutions have been developed over the recent past, most of them are inefficient while others are still susceptible to attacks. In this paper, we leverage on the k-valued modified bilinear inverse Diffie-Hellman problem and one-way hashing function to develop an efficient authentication protocol for IoVs. To demonstrate the robustness of its semantic security, we deploy the Real or Random (ROR) model. In addition, we execute extensive informal security anaysis to show that our scheme resists typical IoVs attacks such as forgery, privileged insider, and replay. Moreover, its performance evaluation shows that it incurs the lowest computation and communication overheads among its peers. Specifically, the proposed protocol reduces the transmission overheads by 8.5%, while increasing the supported security functionalities by 88.9%. It is therefor suitable for deployment in the IoV environment to mitigate the numerous security threats at relatively lower computation and energy costs.

With the widespread application of recommender systems in personalized services, effectively capturing the complex interactions between users and items has become a key issue. Traditional recommender systems often rely on a single type of user-item interaction data, neglecting the multidimensional attributes of users and items and the rich semantic relationships between them. To address this issue, this paper proposes a recommendation method called HRAN-MKR, which combines Heterogeneous Relation Attention Networks (HRAN) with the Multi-task Knowledge Graph-based recommendation (MKR) framework. HRAN dynamically learns the importance of user-item relationship paths through attention mechanisms and selectively aggregates multi-modal features, thereby capturing richer semantic information between users and items. The core motivation of this study is that the relationships between users and items are not homogeneous but consist of various types and strengths of connections, each playing a different role in the recommendation process. By introducing HRAN, we can assign different weights to each relationship path, allowing the model to focus more on those relationships that are more important for predicting user preferences. Furthermore, introducing HRAN helps maintain high computational efficiency and generalization ability when dealing with large-scale and heterogeneous data. Through experiments on multiple real-world datasets, we verify the effectiveness of HRAN-MKR in recommender systems, particularly in handling complex user-item interactions and improving recommendation accuracy. Overall, this paper provides a new perspective for the recommendation system field by enhancing the representation power of GNN modules through relation attention mechanisms for more accurate personalized recommendations.

The Internet of Medical Things (IoMT) holds the promise of cutting costs and improving both efficiency and quality of healthcare systems. In IoMT, billions of sensitive patient physiological data are exchanged among the patients and healthcare facilities. Since open channels are used during these exchanges, attackers can compromise this data, posing serious consequences which can be fatal. Although a myriad of techniques have been developed to mitigate these threats, they are either inefficient or lack some pertinent security features. In this paper, we leverage on lightweight cryptographic primitives to develop an authentication protocol that is not only efficient but also robust against typical IoMT threats. The performance evaluation carried out shows that our approach reduced the computation complexity by 58.26% while thwarting threats such as session key disclosure, privileged insider, offline guessing, replay, side-channeling and session hijacking. In addition, the formal security analysis using Burrows–Abadi–Needham (BAN) logic demonstrates the robustness of the mutual authentication procedures.

Internet of Drones (IoD) plays a critical role in remote monitoring operations. It has been heavily deployed in applications such as traffic management, air traffic control, disaster and agricultural monitoring. In such operations, high volumes of sensitive and private data is collected and transmitted to the ground stations. The usage of unreliable and open wireless channels during the message exchange process exposes the IoD to a myriad of security threats. Therefore, numerous authentication protocols have been developed in the recent past to mitigate these threats. However, most of these protocols are still inadequate in the provision of robust security against typical IoD attacks. Because, they involve resource-intensive operations and make them unsuitable for an IoD environment. Therefore, we put forward a new authentication protocol which exhibits costeffective performance. To develop the new protocol for secure IoD communication, we leverage on biometrics, physically unclonable function and elliptic curve cryptography for the robust performance. Extensive formal security analysis using the Random Oracle Model (ROM) is showing that proposed protocol is provably secure. The informal security analysis is also performed which demonstrates its robustness against all the threat assumptions in the Canetti-Krawczyk (C-K) model. Specifically, it is found to offer conditional privacy, unlinkability, anonymity, mutual authentication, session key establishment and perfect key secrecy. Moreover, it is capable enough to mitigate numerous threats such as forgery, impersonation, stolen verifier, replay, physical capture, de-synchronization, known secret key and smart card loss attacks. The performance is cost-effective as evident from the comparative analysis showing that the proposed protocol incurs the lowest energy consumptions and computation overheads at relatively low communication costs compared to many other authentication schemes.

The evolution of intelligent transport systems (ITS) has significantly enhanced driving experiences in vehicular ad-hoc networks (VANET), crucial for improving road safety and traffic efficiency. Despite these advancements, challenges persist. Trust management solutions often assume user legitimacy without verification. Moreover, the need for reauthentication as vehicles move between RSUs adds computational complexity. VANET is relatively vulnerable to security threats from multiple parties given its highly dynamic topology and its wireless and heterogeneous communication mode. We have expanded upon Maria et al.’s schema by specifically targeting and resolving significant shortcomings in their research. More precisely, we improve the features related to confirming the identity of users, preventing vehicles from being used without authorization, and continuously monitoring their current location in order to strengthen the security of vehicles and increase user involvement. We suggest implementing a schema that utilizes QR code verification to authenticate vehicle users, specifically targeting these difficulties. In VANETs, blockchain has been adopted with the authentication process for fog computing. The result is that there is no need for trusted authority (TA) while achieving privacy within the blockchain network and ensuring of the vehicle user authentication and the integrity of messages. Several key benefits have been achieved, such as blocking malicious entities and the potential damage they cause, enabling the revocation of malicious vehicles by conditional anonymization, as well as simplifying conflict resolution. Likewise, the proposed work was characterized by achieving an effective balance between effectiveness and complexity, protecting privacy, conditional tracking, ensuring integrity, and anonymous authentication.

The growing adoption of the Internet of Things (IoT) terminology in recent years in several domains, including healthcare, has yielded multiple benefits. One of the roles is to support professionals in delivering outstanding and suitable healthcare for patients, while also empowering patients to independently track their health state. However, this wide growth is accompanied by many significant security concerns and cyber risks, including unauthorized access, data leakage, information modification, and data erasure. This article outlines the advantages of employing groundbreaking blockchain technology to provide robust and verified authentication methods for different systems. Additionally, it imparts a foundational comprehension of the technologies employed in the procedure of constructing and advancing blockchain-based platforms. In addition, we explore tools like Scythe and Avispa that are employed to assess the system’s resilience against different types of attacks.

Wireless sensor networks have been deployed in areas such as healthcare, military, transportation and home automation to collect data and forward it to remote users for further processing. Since open wireless communication channels are utilized for data transmissions, the exchanged messages are vulnerable to various threats such as eavesdropping and message falsifications. Therefore, many security solutions have been introduced to address these challenges. However, the resource-constrained nature of the sensor nodes makes it inefficient to deploy the conventional security schemes which require long keys for improved security. Therefore, lightweight authentication protocols have been presented. Unfortunately, majority of these schemes are still insecure while others incur relatively higher energy, computation, communication and storage complexities. In this paper, a protocol that deploys only lightweight one-way hashing and exclusive OR operations is presented. Its formal security analysis using Real-or Random (ROR) model demonstrates its capability to uphold the security of the derived session keys. In addition, its semantic security evaluation shows that it offers user privacy, anonymity, untraceability, authentication, session key agreement and key secrecy. Moreover, it is shown to resist attacks such as side-channeling, physical capture, eavesdropping, offline guessing, spoofing, password loss, session key disclosure, forgery and impersonations. In terms of performance, it has relatively lower communication overheads and improves the computation costs and supported security characteristics by 31.56% and 33.33% respectively.

This study presents a systematic literature review on blockchain-based authentication in smart environments that include smart city, smart home, smart grid, smart healthcare, smart farming and smart transportation. The review incorporated 39 articles presenting blockchain solutions for security and privacy issues through authentication mechanisms in these smart environments. Guided by three research questions to determine the main issues in smart environment, the availability of blockchain-based authentication solutions and identified research gaps and future research endeavors, this review used PRISMA method to provide insights on the use of blockchain-based authentication schemes. The research gap is that blockchain solutions are mostly at the proposal, and sometimes conceptual stage is in the reviewed articles. In addition, solutions presented in smart environments require exploration into blockchain. The findings show similar situational issues across different smart environments and the flexibility and adaptability of blockchain to provide solutions to the identified issues pertaining to security and privacy. More clearly, the authentication problem posed across different smart environments can be adapted to blockchain technology provided that it is combined with other technologies to increase efficiency, despite the existence of large-scale authentication mechanisms. Now, blockchain still has the unique, distributed feature of converting the current database into blockchain databases. This review guided future research directions which could further contribute to the sustainable management of smart environments.

Internet of Vehicles (IoVs) has greatly improved safety and quality of services in Intelligent Transportation System (ITS). However, the deployed Dedicated Short-Range Communication (DSRC) protocol broadcasts messages after every 100-300ms. This presents some challenges in message validation within this short duration. As such, most of the current authentication schemes which incur heavy computation and communication overheads are not suitable in this environment. In this paper, an efficient authentication scheme is presented based on lightweight cryptographic primitives such as collision-resistant one-way hashing functions and exclusive OR (XOR) operations. In our protocol, two-factor authentication is attained using Physically Unclonable Function (PUF) generated identities and random nonces, as well as passwords. Extensive formal security verification using Real or Random (RoR) model shows that it is provably secure. In addition, elaborate semantic security analysis shows that it offers anonymity, untraceability and key secrecy as well as resilience against numerous IoV attack vectors. In terms of performance, comparative evaluations demonstrate that it reduces computation and energy consumptions by 42.31%. Moreover, it increases the supported security features by 26.67%.

Unmanned aerial vehicles offer services such as military reconnaissance in potentially adversarial controlled regions. In addition, they have been deployed in civilian critical infrastructure monitoring. In this environment, real-time and massive data is exchanged between the aerial vehicles and the ground control stations. Depending on the mission of these aerial vehicles, some of the collected and transmitted data is sensitive and private. Therefore, many security protocols have been presented to offer privacy and security protection. However, majority of these schemes fail to consider attack vectors such as side-channeling, de-synchronization and known secret session temporary information leakages. This last attack can be launched upon adversarial physical capture of these drones. In addition, some of these protocols deploy computationally intensive asymmetric cryptographic primitives that result in high overheads. In this paper, an authentication protocol based on lightweight quadratic residues and hash functions is developed. Its formal security analysis is executed using the widely deployed random oracle model. In addition, informal security analysis is carried out to show its robustness under the Dolev–Yao (DY) and Canetti- Krawczyk (CK) threat models. In terms of operational efficiency, it is shown to have relatively lower execution time, communication costs, and incurs the least storage costs among other related protocols. Specifically, the proposed protocol provides a 25% improvement in supported security and privacy features and a 6.52% reduction in storage costs. In overall, the proposed methodology offers strong security and privacy protection at lower execution time, storage and communication overheads.

Abstract Driven by the requirements for entirely low communication latencies, high bandwidths, reliability and capacities, the Fifth Generation (5G) networks has been deployed in a number of countries. One of the most prevalent application scenarios of 5G networks is the Internet of Things (IoT) that can potentially boost convenience and energy savings. However, the information exchanged over the open wireless 5G networks is susceptible to numerous attacks such as malicious modifications. Although many protocols have been developed to protect against these attacks, the provision of optimum security and privacy issues in 5G networks is still an open challenge. This is attributed to the high device density, frequent handovers and resource constrained nature the 5G IoT nodes. In this article, a network selection and authentication protocol that securely verifies the authenticity of all the communicating entities is presented. The network selection is accomplished using Artificial Neural Network (ANN) for increased efficiency. In addition, all the security tokens are independently derived at the end devices without the involvement of any central authority. Formal security analysis based on the Burrows–Abadi–Needham (BAN) logic shows that all the terminals securely authenticate each other before the onset of packet exchanges. In addition, it is shown that this protocol thwarts majority of the conventional 5G attack vectors and is robust under the Dolev–Yao (DY) threat model. Moreover, a comparison with other related schemes shows that the proposed protocol offers many adorable security features at relatively low communication and computation costs. The simulation results show that the deployed ANN yields low packet loss ratio and latency variations.