In recent times, the emergence of the Metaverse has garnered worldwide attention as an innovative digital space that holds immense potential to provide a wide range of health services to medical professionals and patients. With increasing stress on healthcare systems, it has become crucial to explore the latest and cost-effective solutions that can provide fast and reliable medical services. The focus of this study, therefore, is to explore applications of metaverse in various health care systems and elaborate on how it can efficiently improve the clinical management of patients. Consequently, an in-depth assessment of the metaverse has been carried out, while covering its core fundamentals, key technologies, and diverse applications in healthcare and medicine, including but not limited to, emergency response learning, hands-on experience in anatomy learning, orthopaedics, paediatrics and so on. To carry out the study, we have used an exploratory approach to analyze qualitative data on healthcare metaverse services in our systematic review. Relevant articles from scientific databases such as Web of Science, Springer, Scopus, and IEEE have been identified, and the analysis has been conducted using the PRISMA reporting guideline to ensure transparent and comprehensive reporting. The results of the study suggest that the metaverse has the potential to transform healthcare systems by introducing novel methods for delivering healthcare services. Metaverse’s AR/VR technologies can enable remote medical consultations and training, benefiting patients and healthcare professionals. Additionally, patients can access health-related information and resources, empowering them to manage their health better and make more informed decisions.

The recent advancements in security approaches have significantly increased the ability to identify and mitigate any type of threat or attack in any network infrastructure, such as a software-defined network (SDN), and protect the internet security architecture against a variety of threats or attacks. Machine learning (ML) and deep learning (DL) are among the most popular techniques for preventing distributed denial-of-service (DDoS) attacks on any kind of network. The objective of this systematic review is to identify, evaluate, and discuss new efforts on ML/DL-based DDoS attack detection strategies in SDN networks. To reach our objective, we conducted a systematic review in which we looked for publications that used ML/DL approaches to identify DDoS attacks in SDN networks between 2018 and the beginning of November 2022. To search the contemporary literature, we have extensively utilized a number of digital libraries (including IEEE, ACM, Springer, and other digital libraries) and one academic search engine (Google Scholar). We have analyzed the relevant studies and categorized the results of the SLR into five areas: (i) The different types of DDoS attack detection in ML/DL approaches; (ii) the methodologies, strengths, and weaknesses of existing ML/DL approaches for DDoS attacks detection; (iii) benchmarked datasets and classes of attacks in datasets used in the existing literature; (iv) the preprocessing strategies, hyperparameter values, experimental setups, and performance metrics used in the existing literature; and (v) current research gaps and promising future directions.

Vehicular ad hoc networks (VANETs) have become increasingly common in recent years due to their critical role in the field of smart transportation by supporting Vehicle-to-Vehicle and Vehicle-to-Infrastructure communication. The security and privacy of VANET are of the utmost importance due to the use of an open wireless communication medium where messages are exchanged in plain text, something which allows attackers to intercept, tamper, replay, and delete them. Hence, there is a high probability that the safety of a VANET-based smart transportation system could be compromised. Nowadays, securing and safeguarding the exchange of messages in VANETs is the focus of many security research teams, as reflected by the number of authentication and privacy schemes that have been proposed. However, these schemes have not fulfilled all aspects of the security and privacy requirements. The present paper is an effort to provide a thorough background on VANETs and their components; various types of attacks on them; and all the security and privacy requirements for authentication and privacy schemes for VANETs. This paper is among the first to provide a comprehensive survey of the existing authentication and privacy schemes and compare them based on all security and privacy requirements, computational and communicational overheads, and the level of resistance to different types of attacks. It also provides a qualitative comparison with the existing surveys. This paper could serve as a guide and reference in the design and development of any new security and privacy techniques for VANETs.

The proliferation of Internet of Things (IoT) devices has brought about an increased threat of botnet attacks, necessitating robust security measures. In response to this evolving landscape, deep learning (DL)-based intrusion detection systems (IDS) have emerged as a promising approach for detecting and mitigating botnet activities in IoT environments. Therefore, this paper thoroughly reviews existing literature on botnet detection in the IoT using DL-based IDS. It consolidates and analyzes a wide range of research papers, highlighting key findings, methodologies, advancements, shortcomings, and challenges in the field. Additionally, we performed a qualitative comparison with existing surveys using author-defined metrics to underscore the uniqueness of this survey. We also discuss challenges, limitations, and future research directions, emphasizing the distinctive contributions of our review. Ultimately, this survey serves as a guideline for future researchers, contributing to the advancement of botnet detection methods in IoT environments and enhancing security against botnet threats.

The security of the internet is seriously threatened by a distributed denial of service (DDoS) attacks. The purpose of a DDoS assault is to disrupt service and prevent legitimate users from using it by flooding the central server with a large number of messages or requests that will cause it to reach its capacity and shut down. Because it is carried out by numerous bots that are managed (infected) by a single botmaster using a fake IP address, this assault is dangerous because it does not involve a lot of work or special tools. For the purpose of identifying and analyzing DDoS attacks, this paper will discuss various machine learning (ML) and deep learning (DL) techniques. Additionally, this study analyses and comparatives the significant distinctions between ML and DL techniques to aid in determining when one of these techniques should be used.

Internet of Things (IoT) paved the way for devices and machine communication using TCP/IP protocol. Lightweight and stateless communication is imperative especially in a situation requiring conservation of energy usage, e.g. wireless sensor network. Representational State Transfer (REST) API method is based on web communication protocol, Hyper-Text Transfer Protocol (HTTP), and is widely used in IoT messaging. Some of these protocols are DPWS, XMPP, MQTT, COaP, AMQP. Among these protocols, MQTT is the most preferred protocol and is expected to be the de facto messaging IoT standard. MQTT uses a publisher/subscriber model to facilitate messaging between devices making messaging lightweight. Nevertheless, there are a number of security issues due to the design of the protocol itself. Some of the issues are denial of service, identity spoofing, information disclosure, elevation of privileges and data tampering. These issues can be caused by both internal and external perpetrators. Researchers have proposed various security techniques and mechanisms to address these issues. Incorporation of security has added processing overhead to the devices and this will have a bearing on IoT devices that are powered by a battery. This issue has opened up new research challenges in making the protocols more lightweight and at the same time not compromising the level of security provided.