Anchal Garg

While the fifth-generation (5G) wireless networks are still being studied, there is already speculation about sixth-generation (6G) echo systems. We understand that 5G is still new to the world, under proper implementation, and might have numerous problems. However, we must continue moving toward the better and the issues faced by 5G can be addressed while working with the 6G technology.

To increase security and privacy in 6G networks, we investigated the possible impact of security on wireless systems, found concerns with different technologies, and provided remedies. The limitations of 5G networks have been realized as more and more 5G networks have been deployed, which surely encourages additional research into 6G networks as the next-generation answer. More and more 5G networks are being deployed. The global deployment of 5G communication has been delayed owing to network security issues. As a result, research into 6G security analysis is critically required.

The 6G of wireless technology is the successor of 5G. This technology is still in development at various scientific and educational institutions. The 6G technology works on untapped radio frequencies and uses multiple cognitive technologies like artificial intelligence (AI) for implementation. The 6G technology is an upgrade, not only to the technological world but also to the standard of living for people. That is why many countries have shown interest in this wireless technology.

This article analyzes potential cyber-defense options for 5G and upcoming 6G networks. After thoroughly reviewing existing literature and industry practices, we discuss proactive approaches such as network segmentation, encryption protocols, intrusion detection systems, and AI-powered threat intelligence.

Advanced mobile network connectivity is being fueled by the rapid evolution of 5G and the forthcoming 6G technologies. This however has exposed mobile networks to new cyber threats and security vulnerabilities. Consequently, cyber resilience, that is, the cope to prepare, identify, respond and recover from cyber-related incidents has become crucial. This paper focuses on a cyber resilience framework for mobile networks utilizing machine learning (ML) aiming at emerging threats. Machine Learning supervised, unsupervised and deep learning algorithms can perform anomaly detection, intrusion detection, prediction and automated threat response systems. Major ones like IDS and anomaly detection are discussed and analyzed with practical instances. The study examines and proposes federated learning, reinforcement learning and explainable AI (XAI) suffice in addressing issues of scarcity of data, time-sensitive processing, and emerging cyber threats. Integrating IoT, edge computers and 6G networks can also improve resilience. It is evident that there is great potential for cyber resilience through machine learning however it has been suggested that standardization, benchmarking and effective test frameworks are put in place.

With the approaching implementation of 6G wireless communication networks, there will be opportunities and problems that have never been seen before, notably in privacy and security. In an era of hyper-connected, intelligent, and data-driven communication, this abstract investigates the cutting-edge technologies set to strengthen the integrity of 6G networks and protect users' privacy. Innovative methods, such as homomorphic encryption, differential privacy, and secure multi-party computation, are being investigated to protect the privacy of users. These approaches make it possible to process data without compromising sensitive information, which is in line with the growing desire for technology that protects individuals' privacy. In addition, biometric authentication serves as the primary form of verification, offering an additional layer of identity verification that is both robust and tailored in comparison to traditional approaches. As a result of the fact that it is anticipated that 6G networks would make use of network slicing, security measures are dynamically altered through isolated slices to meet several different service requirements. Wireless transmissions can be made more secure by the utilization of modern beamforming and signal processing techniques, which are part of the physical layer security. Traditional trust assumptions are called into question by the paradigm of zero-trust security models, which advocates for continuous authentication and authorization. This chapter provides a glimpse into the disruptive technologies that are going to determine the landscape of privacy and security in 6G wireless communication networks. These developments are critically important in laying a foundation that is secure, trustworthy, and privacy-focused for the hyper-connected future of wireless communication. The old ways of trusting people are being reconsidered because of a new security approach called zero-trust. Biometric authentication has an extra layer of security and can be more personalized than traditional methods. This approach suggests always checking and allowing access to systems and data, instead of relying on trust. This story shows how new technologies are changing the way we keep things private and safe in 6G wireless networks. These advancements are the basis for a future that is safe, dependable, and focused on privacy in wireless communication.

In recent years, there has been a substantial increase in the amount of attention paid to the use of machine learning strategies within the area of medical data. Classification and clustering algorithms are two major kinds of machine learning algorithms that are frequently utilized for a variety of tasks in medical data analysis. These algorithms are employed in a variety of different ways. Clustering algorithms are used to group data points that are similar together based on their similarity or distance metrics, while classification algorithms are used to predict the class or category of a new data point based on the patterns learned from labeled data. Classification methods may be found in machine learning software. We present an overview of classification and clustering algorithms for medical data in this article, covering its uses, problems, and future approaches, among other things.

Learner diversity is a matter for universities enrolling international students. Learner engagement then becomes a major concern for instructors. This study uses gamification techniques to determine its impact on engagement and learning when it comes to diverse cohort of learners. A survey questionnaire method was used to identify the effect of gamification on engagement, motivation, performance, and learning. This empirical study revealed that quizzes supported by gamification enhanced engagement, performance and learning but did not motivate learners to study outside the lecture theater. This paper also summarizes the issues while using gamified quizzes in the classroom settings and provides potential solutions.