The fifth-generation (5G) technology-enabled vehicular network has been widely used in intelligent transportation in recent years. Since messages shared among vehicles are always broadcasted by openness environment’ nature, which is vulnerable to several privacy and security problems. To cope with this issue, several researchers have proposed pseudonym authentication schemes for the 5G-enabled vehicular network. Nevertheless, these schemes applied complected and time-consumed operations. Therefore, this paper proposes a fog computing-based pseudonym authentication (FC-PA) scheme to decrease the overhead of performance in 5G-enabled vehicular networks. The FC-PA scheme applies only one scalar multiplication operation of elliptic curve cryptography to prove information. A security analysis of our work explains that our scheme satisfies privacy-preserving and pseudonym authentication, which are resilient against common security attacks. With performance efficiency, our work can obtain better trade-offs between efficiency and security than the well-known recent works.

Context : Quantum Cryptography (QC) revolutionizes network communication – harnessing principles of quantum mechanics to enable the exchange of encrypted messages – for enabling secure data transmission in an era of quantum information processing. With a significant rise in quantum computing research and development efforts, there is an increasing interest in exploring QC (e.g., Quantum Key Distribution (QKD) and Quantum Secured Encryption (QSE)) against a multitude of security threats in futuristic networks for quantum information processing. Objectives and Method : The objective of this study is to review the existing research i.e., consolidating the published evidence, that streamlines and documents the predominant challenges, recurring solutions, security threats, and their counter-measures against the outlined research questions in the context of QC. To conduct this study, we followed the guidelines and method of Systematic Literature Reviews (SLRs) to answer seven research questions. These questions investigate the proposed solutions for state-of-the-art QC and its impact on future network security. Results and Implications : Based on the seven (7) outlined research questions, this study systematically selected and reviewed one hundred and thirty four (134) research studies published from 2016 to 2023 with a focus on QC for quantum information processing. The results of this SLR establish a knowledge base for modern QC applications to guarantee network security in quantum-enabled network communications. The review reveals that though still in the phase of its inception, the research on QC is progressing rapidly, highlighting the necessity for network protocol and frameworks to cater for quantum network security. The SLR also highlights the challenges encountered while designing or implementing the QC systems, pinpointing the significance of keeping abreast of QKD networks and addressing possible ramifications for internet security in the future. The SLR provides theoretical foundations and evidence-based guidelines to tackle emerging and futuristic challenges of security in the context of QC and QKD for quantum information processing.

Several researchers have proposed secure authentication techniques for addressing privacy and security concerns in the fifth-generation (5G)-enabled vehicle networks. To verify vehicles, however, these conditional privacy-preserving authentication (CPPA) systems required a roadside unit, an expensive component of vehicular networks. Moreover, these CPPA systems incur exceptionally high communication and processing costs. This study proposes a CPPA method based on fog computing (FC), as a solution for these issues in 5G-enabled vehicle networks. In our proposed FC-CPPA method, a fog server is used to establish a set of public anonymity identities and their corresponding signature keys, which are then preloaded into each authentic vehicle. We guarantee the security of the proposed FC-CPPA method in the context of a random oracle. Our solutions are not only compliant with confidentiality and security standards, but also resistant to a variety of threats. The communication costs of the proposal are only 84 bytes, while the computation costs are $0.0031$, $2.0185$ to sign and verify messages. Comparing our strategy to similar ones reveals that it saves time and money on communication and computing during the performance evaluation phase.

Recently, Wireless Sensor Network (WSN) technology has emerged extensively. This began with the deployment of small-scale WSNs and progressed to that of larger-scale and Internet of Things-based WSNs, focusing more on energy conservation. Network clustering is one of the ways to improve the energy efficiency of WSNs. Network clustering is a process of partitioning nodes into several clusters before selecting some nodes, which are called the Cluster Heads (CHs). The role of the regular nodes in a clustered WSN is to sense the environment and transmit the sensed data to the selected head node; this CH gathers the data for onward forwarding to the Base Station. Advantages of clustering nodes in WSNs include high callability, reduced routing delay, and increased energy efficiency. This article presents a state-of-the-art review of the available optimization techniques, beginning with the fundamentals of clustering and followed by clustering process optimization, to classifying the existing clustering protocols in WSNs. The current clustering approaches are categorized into meta-heuristic, fuzzy logic, and hybrid based on the network organization and adopted clustering management techniques. To determine clustering protocols’ competency, we compared the features and parameters of the clustering and examined the objectives, benefits, and key features of various clustering optimization methods.

The privacy and security of the information exchanged between automobiles in 5G-enabled vehicular networks is at risk. Several academics have offered a solution to these problems in the form of an authentication technique that uses an elliptic curve or bilinear pair to sign messages and verify the signature. The problem is that these tasks are lengthy and difficult to execute effectively. Further, the needs for revoking a pseudonym in a vehicular network are not met by these approaches. Thus, this research offers a fog computing strategy for 5G-enabled automotive networks that is based on the Chebyshev polynomial and allows for the revocation of pseudonyms. Our solution eliminates the threat of an insider attack by making use of fog computing. In particular, the fog server does not renew the signature key when the validity period of a pseudonym-ID is about to end. In addition to meeting privacy and security requirements, our proposal is also resistant to a wide range of potential security breaches. Finally, the Chebyshev polynomial is used in our work to sign the message and verify the signature, resulting in a greater performance cost efficiency than would otherwise be possible if an elliptic curve or bilinear pair operation had been employed.

Machine learning is a branch of computing that studies the design of algorithms with the ability to “learn.” A subfield would be deep learning, which is a series of techniques that make use of deep artificial neural networks, that is, with more than one hidden layer, to computationally imitate the structure and functioning of the human organ and related diseases. The analysis of health interest images with deep learning is not limited to clinical diagnostic use. It can also, for example, facilitate surveillance of disease-carrying objects. There are other examples of recent efforts to use deep learning as a tool for diagnostic use. Chest X-rays are one approach to identify tuberculosis; by analysing the X-ray, you can spot any abnormalities. A method for detecting the presence of tuberculosis in medical X-ray imaging is provided in this paper. Three different classification methods were used to evaluate the method: support vector machines, logistic regression, and nearest neighbors. Cross-validation and the formation of training and test sets were the two classification scenarios used. The acquired results allow us to assess the method’s practicality.