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The rapid progress in digitalization and automation have led to an accelerated growth in healthcare, generating novel models that are creating new channels for rendering treatment at reduced cost. The Metaverse is an emerging technology in the digital space which has huge potential in healthcare, enabling realistic experiences to the patients as well as the medical practitioners. The Metaverse is a confluence of multiple enabling technologies such as artificial intelligence, virtual reality, augmented reality, internet of medical devices, robotics, quantum computing, etc. through which new directions for providing quality healthcare treatment and services can be explored. The amalgamation of these technologies ensures immersive, intimate and personalized patient care. It also provides adaptive intelligent solutions that eliminates the barriers between healthcare providers and receivers. This article provides a comprehensive review of the Metaverse for healthcare, emphasizing on the state of the art, the enabling technologies to adopt the Metaverse for healthcare, the potential applications, and the related projects. The issues in the adaptation of the Metaverse for healthcare applications are also identified and the plausible solutions are highlighted as part of future research directions.INDEX TERMS Metaverse, healthcare, virtual reality, digital twin, cybersecurity.

The widespread acceptance and increase of the Internet and mobile technologies have revolutionized our existence. On the other hand, the world is witnessing and suffering due to technologically aided crime methods. These threats, including but not limited to hacking and intrusions and are the main concern for security experts. Nevertheless, the challenges facing effective intrusion detection methods continue closely associated with the researcher’s interests. This paper’s main contribution is to present a host-based intrusion detection system using a C4.5-based detector on top of the popular Consolidated Tree Construction (CTC) algorithm, which works efficiently in the presence of class-imbalanced data. An improved version of the random sampling mechanism called Supervised Relative Random Sampling (SRRS) has been proposed to generate a balanced sample from a high-class imbalanced dataset at the detector’s pre-processing stage. Moreover, an improved multi-class feature selection mechanism has been designed and developed as a filter component to generate the IDS datasets’ ideal outstanding features for efficient intrusion detection. The proposed IDS has been validated with state-of-the-art intrusion detection systems. The results show an accuracy of 99.96% and 99.95%, considering the NSL-KDD dataset and the CICIDS2017 dataset using 34 features.

SUMMARYMulti-hop routing in an open environment in the absence of well-established infrastructure and centralized authority strives for trustworthiness and cooperation of nodes in a Mobile Ad-hoc Network (MANET). There is no guarantee of secure and reliable delivery of packets when some internal nodes intentionally perform packet forwarding misbehavior by compromising the routing mechanism. In this paper, we address this issue with a trust-model integrated with an attack pattern discovery technique. Extended from the Ad-hoc On-demand Distance Vector (AODV) routing protocol, we propose a trust-based scheme founded on nodes' historical behaviors which adopts a pattern discovery mechanism in order to detect suspicious activities from the malevolent nodes before they start dropping data packets. We also present the detailed mode of operations of three distinct adversary models launching various kinds of packet forwarding misbehavior. Theoretical analysis and experimental results prove that the integration of a pattern discovery method with a trust-based model provides earlier detection of adversaries which follow specific attack patterns and weakens their damaging effects on the network in comparison to a solitary trust-based model.

Vehicular Ad-hoc networks (VANETs) require trusted vehicles to vehicles communication. VANET is multidimensional network in which the vehicles continuously change their locations. Secure routing is imperative during the routing process to incorporate mutual trust between these nodes. Sometimes, the malicious node broadcast the bogus information among other nodes. Establishing trust is a challenge while one or more malicious nodes attempt to disrupt route discovery or data transmission in the network. A lot of research has been carried out for secure routing process with trust-based approaches. In this paper, we present survey of various mechanisms to improve different ad-hoc routing protocols for secure routing process by enhancing the trust among different nodes in VANETs.

Supervised learning and pattern recognition is a crucial area of research in information retrieval, knowledge engineering, image processing, medical imaging, and intrusion detection. Numerous algorithms have been designed to address such complex application domains. Despite an enormous array of supervised classifiers, researchers are yet to recognize a robust classification mechanism that accurately and quickly classifies the target dataset, especially in the field of intrusion detection systems (IDSs). Most of the existing literature considers the accuracy and false-positive rate for assessing the performance of classification algorithms. The absence of other performance measures, such as model build time, misclassification rate, and precision, should be considered the main limitation for classifier performance evaluation. This paper’s main contribution is to analyze the current literature status in the field of network intrusion detection, highlighting the number of classifiers used, dataset size, performance outputs, inferences, and research gaps. Therefore, fifty-four state-of-the-art classifiers of various different groups, i.e., Bayes, functions, lazy, rule-based, and decision tree, have been analyzed and explored in detail, considering the sixteen most popular performance measures. This research work aims to recognize a robust classifier, which is suitable for consideration as the base learner, while designing a host-based or network-based intrusion detection system. The NSLKDD, ISCXIDS2012, and CICIDS2017 datasets have been used for training and testing purposes. Furthermore, a widespread decision-making algorithm, referred to as Techniques for Order Preference by Similarity to the Ideal Solution (TOPSIS), allocated ranks to the classifiers based on observed performance reading on the concern datasets. The J48Consolidated provided the highest accuracy of 99.868%, a misclassification rate of 0.1319%, and a Kappa value of 0.998. Therefore, this classifier has been proposed as the ideal classifier for designing IDSs.

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