Nowadays, with rapid advancements of vehicular telematics and communication techniques, proliferation of vehicular ad hoc networks (VANETs) have been witnessed, which facilitates the construction of promising intelligent transportation system (ITS). Due to inherent wireless communicating features in open environment, secure transmission among numerous VANET entities remains crucial issues. Currently, lots of research efforts have been made, while most of which tend to allocate the universal group key to the verified devices for both vehicle-to-vehicle (V2V) and vehicle-to-RSU (V2R) communications. However, in heterogeneous VANET environment with large numbers of devices in same vehicular group, complicated and variable topologies lead to continuous key updating in every moment, causing interference to regular V2R data exchange, which is not reliable and efficient for resource-constrained VANET environment. Moreover, group membership recording and detecting mechanisms are necessary for real time vehicle revocation and participation, which has not been further studied so far. In this paper, we address the above issues by proposing a secure authentication and key management scheme. In our design, novel VANET system model with edge computing infrastructure is adopted so as to offer adequate computing and storing capacity compared to traditional VANET structure. Note that our certificateless authentication scheme applies the independent session key for each vehicle for interference avoidance. Furthermore, consortium blockchain is employed for V2V group key construction. Real time group membership arrangement with efficient group key updating is accordingly provided. Formal security proofs are presented, demonstrating that the proposed scheme can achieve desired security properties. Performance analysis is conducted as well, proving that the proposed scheme is efficient compared with the state-of-the-arts.

Body area networks (BANs) have emerged as an enabling technique for e-healthcare systems, which can be used to continuously and remotely monitor patients' health. In BANs, the data of a patient's vital body functions and movements can be collected by small wearable or implantable sensors and sent using short-range wireless communication techniques. Due to the shared wireless medium between the sensors in BANs, it may be possible to have malicious attacks on e-healthcare systems. The security and privacy issues of BANs are becoming more and more important. To provide secure and correct association of a group of sensors with a patient and satisfy the requirements of data confidentiality and integrity in BANs, we propose a novel enhanced secure sensor association and key management protocol based on elliptic curve cryptography and hash chains. The authentication procedure and group key generation are very simple and efficient. Therefore, our protocol can be easily implemented in the power and resource constrained sensor nodes in BANs. From a comparison of results, furthermore, we can conclude that the proposed protocol dramatically reduces the computation and communication cost for the authentication and key derivation compared with previous protocols. We believe that our protocol is attractive in the application of BANs.

Nowadays, with the purpose of fulfilling the increasing demands for emerging extensive and ubiquitous accessibility to modern intelligent transportation systems (ITS), the conventional vehicle-to-everything (V2X) paradigms are progressively evolving into the Internet of vehicles (IoVs). As of now, substantial enhancements in IoV technologies have been witnessed, particularly in the era of vehicular data secure exchange and user privacy protection. On the other hand, unmanned aerial vehicles (UAVs) are envisioned to provide scalable and adaptive stereoscopic service coverage for IoV implementations. However, the existing approaches intended for the common scenarios mainly rely on the well-established local IoV infrastructures, whereas the specific infrastructure-less IoVs with dysfunctional edge facilities have not been properly investigated. Additionally, the related UAV-assisted IoV schemes still fail to independently perform the secure vehicular data transmission but rather as the auxiliary strategies due to the dependence on the assistance of local edge deployment. Therefore, the practical requirements of reliability and scalability in real-world IoV circumstances cannot be satisfied. In this paper, an efficient UAV certificateless group authentication mechanism is developed in order to facilitate the secure data transmission of infrastructure-less IoV. The proposed design deploys the tethered UAV (TUAV) as the specific mobilized base station so that the active edge IoV infrastructure is not needed. The security analysis regarding the key security features are presented first, followed by the performance evaluation. According to the comparison results with the existing designs, improvements in terms of computational cost and communication overhead for different phases can be demonstrated.

A current trend of research focuses on artificial intelligence based cryptography which although proposed almost thirty years ago could not attract much attention. Abadi and Anderson’s work on adversarial cryptography in 2016 rejuvenated the research area which now focuses in building neural networks that are able to learn cryptography using the idea from Generative Adversarial Networks (GANs). In this paper, we survey the most prominent research works that cover neural networks based cryptography from two main periods. The first period covers the oldest models that have been proposed shortly after 2000 and the second period covers the more recent models that have been proposed since 2016. We first discuss the implementation of the systems from the earlier era and the attacks mounted on them. After that, we focus on post 2016 era where more advanced techniques are utilized that rely on GANs in which neural networks compete with each other in order to achieve a goal e.g. learning to encrypt a communication. Finally, we discuss security analysis performed on adversarial cryptography models.