The histrionic growth of mobile subscribers, disruptive ecosystems such as IoT-based applications, and astounding channel capacity requirements to connect trillions of devices are massive challenges of the earlier mobile generations, 5G turned up the key solution. The prime objective of the 5G network is not only to maintain a 1000-fold capacity gain and 10 Giga Bits per second delivered to a single user, but it also assured quality-of-service, higher spectral efficiency, the ultra-reliable and improved battery lifetime of devices and massive machine-type communication (mMTC). The huge traffic load and high amount of resource consumption in 5G applications, augmented reality and virtual reality for magnificent virtual experience, and wireless body area networks will seriously affect the channel capacity of cellular cells and interrupt the admission and service of other users which makes compulsory new means of channel capacity and spectral efficiency enhancement techniques. In this research, we review several key emerging wireless technologies to increase channel capacity and spectral efficiency that will not only lead to improve network performance but also meets the ever-increasing user demands. We investigate various benefits and current research challenges of using these technologies. We analyze massive multi-input multi-output technology (mMIMO) an efficient technique and promising solution for the 5G and Beyond 5G (B5G) networks with several benefits and features. Moreover, this paper will be of vast help to the researchers who will involve advance investigation and also to the wireless network operator industry that is in the search for smooth development of state-of-the-art 5G and B5G networks.

Fog computing is a promising paradigm that can mitigate the heavy burden on cloud-central processing of the vast amount of IoT data. Although fog computing has the advantages of low latency, storage, and computing resources that serve IoT applications and things, it hardly suffers from security and privacy challenges. Proxy re-encryption (PRE) is an effective cryptographic solution to ensure the security of fog-to-things communication. However, in PRE schemes, the problem of a processing delay caused by offloading significant computational load to the proxy for re-encryption, and the heavy computation operations of data owner encryption and user decryption due to asymmetric cryptographic use, are still unresolved in the literature. In this paper, we propose a hybrid proxy re-encryption scheme that combines lightweight symmetric and asymmetric encryption algorithms to establish secure communications in fog-to-things computing. In the proposed scheme, the computational cost incurred by fog nodes to carry out the re-encryption process is highly efficient. Meanwhile, the scheme can reduce the encryption and decryption overheads for end-users with resource-constrained devices. Security and performance analyses are conducted, and the results indicate that our scheme is secure, highly efficient, and lightweight.

Online banking has become increasingly popular in recent years, making it a target for cyberattacks. Banks have implemented various user authentication methods to protect their customers’ online accounts. This paper reviews the state-of-the-art user authentication methods used in online banking and potential cyber threats. This paper starts by exploring different user authentication methods, such as knowledge-based authentication (KBA), biometrics-based authentication (BBA), possession-based authentication (PBA), and other methods. The advantages and disadvantages of each user authentication method are then discussed. Furthermore, the paper discusses the various cyber threats that can compromise user authentication for online banking systems, such as malware attacks, social engineering, phishing attacks, man-in-the-middle (MiTM) attacks, denial of service (DoS) attacks, session hijacking, weak passwords, keyloggers, SQL injection, and replay attacks. Also, the paper explores the user authentication methods used by popular banks, which can provide insights into best practices for safeguarding online banking accounts and future user authentication methods in online banking and cyber threats. It states that the increasing use of BBA, two-factor authentication (2FA), and multi-factor authentication (MFA) will help improve the security of online banking systems. However, the paper also warns that new cyber challenges will emerge, and banks need to be vigilant in protecting their customers’ online banking accounts.

In the realm of wireless sensor networks (WSNs), preserving data integrity, privacy, and security against cyberthreats is paramount. Proxy re-encryption (PRE) plays a pivotal role in ensuring secure intra-network communication. However, existing PRE solutions encounter persistent challenges, including processing delays due to the transfer of substantial data to the proxy for re-encryption and the computational intensity of asymmetric cryptography. This study introduces an innovative PRE scheme that is meticulously customized for WSNs to enhance the secure communication between nodes within the network and external data server. The proposed PRE scheme optimizes efficiency by integrating lightweight symmetric and asymmetric cryptographic techniques, thereby minimizing computational costs during PRE operations and conserving energy for resource-constrained nodes. In addition, the scheme incorporates sophisticated key management and digital certificates to ensure secure key generation and distribution, which in turn, facilitates seamless authentication and scalable data sharing among the entities in WSN. This scheme maintains sensor-node data encryption and delegates secure re-encryption tasks exclusively to cluster heads, thereby reinforcing data privacy and integrity. Comprehensive evaluations of security, performance, and energy consumption validated the robustness of the scheme. The results confirm that the proposed PRE scheme significantly enhances the security, efficiency, and overall network lifetime of WSNs.

Securing digital images is becoming an important concern in today’s information security due to the extensive use of secure images that are either transmitted over a network or stored on disks. Image encryption is the most effective way to fulfil confidentiality and protect the privacy of images. Nevertheless, owing to the large size and complex structure of digital images, the computational overhead and processing time needed to carry out full image encryption prove to be limiting factors that inhibit it of being used more heavily in real time. To solve this problem, many recent studies use the selective encryption approach to encrypt significant parts of images with a hope to reduce the encryption overhead. However, it is necessary to realistically evaluate its performance compared to full encryption. In this paper, we study the performance and efficiency of image segmentation methods used in the selective encryption approach, such as edges and face detection methods, in determining the most important parts of visual images. Experiments were performed to analyse the computational results obtained by selective image encryption compared to full image encryption using symmetric encryption algorithms. Experiment results have proven that the selective encryption approach based on edge and face detection can significantly reduce the time of encrypting still visual images as compared to full encryption. Thus, this approach can be considered a good alternative in the implementation of real-time applications that require adequate security levels.