IoT and AI created a Transportation Management System, resulting in the Internet of Vehicles. Intelligent vehicles are combined with contemporary communication technologies (5G) to achieve automated driving and adequate mobility. IoV faces security issues in the next five (5) areas: data safety, V2X communication safety, platform safety, Intermediate Commercial Vehicles (ICV) safety, and intelligent device safety. Numerous types of AI models have been created to reduce the outcome infiltration risks on ICVs. The need to integrate confidence, transparency, and repeatability into the creation of Artificial Intelligence (AI) for the safety of ICV and to deliver harmless transport systems, on the other hand, has led to an increase in explainable AI (XAI). Therefore, the space of this analysis protected the XAI models employed in ICV intrusion detection systems (IDSs), their taxonomies, and available research concerns. The study s findings demonstrate that, despite its relatively recent submission to ICV, XAI is a potential explore area for those looking to increase the net effect of ICVs. The paper also demonstrates that XAI s greater transparency will help it gain acceptance in the vehicle industry.

The boundaries between the real world and the virtual world are going to be blurred by Metaverse. It is transforming every aspect of humans to seamlessly transition from one virtual world to another. It is connecting the real world with the digital world by integrating emerging tech like 5G, 3d reconstruction, IoT, Artificial intelligence, digital twin, augmented reality (AR), and virtual reality (VR). Metaverse platforms inherit many security \& privacy issues from underlying technologies, and this might impede their wider adoption. Emerging tech is easy to target for cybercriminals as security posture is in its infancy. This work elaborates on current and potential security, and privacy risks in the metaverse and put forth proposals and recommendations to build a trusted ecosystem in a holistic manner.

Artificial Intelligence used in future networks is vulnerable to biases, misclassifications, and security threats, which seeds constant scrutiny in accountability. Explainable AI (XAI) methods bridge this gap in identifying unaccounted biases in black-box AI/ML models. However, scaffolding attacks can hide the internal biases of the model from XAI methods, jeopardizing any auditory or monitoring processes, service provisions, security systems, regulators, auditors, and end-users in future networking paradigms, including Intent-Based Networking (IBN). For the first time ever, we formalize and demonstrate a framework on how an attacker would adopt scaffoldings to deceive the security auditors in Network Intrusion Detection Systems (NIDS). Furthermore, we propose a detection method that auditors can use to detect the attack efficiently. We rigorously test the attack and detection methods using the NSL-KDD. We then simulate the attack on 5G network data. Our simulation illustrates that the attack adoption method is successful, and the detection method can identify an affected model with extremely high confidence.

Currently, research on 5G communication is focusing increasingly on communication techniques. The previous studies have primarily focused on the prevention of communications disruption. To date, there has not been sufficient research on network anomaly detection as a countermeasure against on security aspect. 5g network data will be more complex and dynamic, intelligent network anomaly detection is necessary solution for protecting the network infrastructure. However, since the AI-based network anomaly detection is dependent on data, it is difficult to collect the actual labeled data in the industrial field. Also, the performance degradation in the application process to real field may occur because of the domain shift. Therefore, in this paper, we research the intelligent network anomaly detection technique based on domain adaptation (DA) in 5G edge network in order to solve the problem caused by data-driven AI. It allows us to train the models in data-rich domains and apply detection techniques in insufficient amount of data. For Our method will contribute to AI-based network anomaly detection for improving the security for 5G edge network.

The integration of IoT with cellular wireless networks is expected to deepen as cellular technology progresses from 5G to 6G, enabling enhanced connectivity and data exchange capabilities. However, this evolution raises security concerns, including data breaches, unauthorized access, and increased exposure to cyber threats. The complexity of 6G networks may introduce new vulnerabilities, highlighting the need for robust security measures to safeguard sensitive information and user privacy. Addressing these challenges is critical for 5G networks massively IoT-connected systems as well as any new ones that that will potentially work in the 6G environment. Artificial Intelligence is expected to play a vital role in the operation and management of 6G networks. Because of the complex interaction of IoT and 6G networks, Explainable Artificial Intelligence (AI) is expected to emerge as an important tool for enhancing security. This study presents an AI-powered security system for the Internet of Things (IoT), utilizing XGBoost, Shapley Additive, and Local Interpretable Model-agnostic explanation methods, applied to the CICIoT 2023 dataset. These explanations empowers administrators to deploy more resilient security measures tailored to address specific threats and vulnerabilities, improving overall system security against cyber threats and attacks.

The development of 5G, cloud computing, artificial intelligence (AI) and other new generation information technologies has promoted the rapid development of the data center (DC) industry, which directly increase severe energy consumption and carbon emissions problem. In addition to traditional engineering based methods, AI based technology has been widely used in existing data centers. However, the existing AI model training schemes are time-consuming and laborious. To tackle this issues, we propose an automated training and deployment platform for AI modes based on cloud-edge architecture, including the processes of data processing, data annotation, model training optimization, and model publishing. The proposed system can generate specific models based on the room environment and realize standardization and automation of model training, which is helpful for large-scale data center scenarios. The simulation and experimental results show that the proposed solution can reduce the time required of single model training by 76.2\%, and multiple training tasks can run concurrently. Therefore, it can adapt to the large-scale energy-saving scenario and greatly improve the model iteration efficiency, which improves the energy-saving rate and help green energy conservation for data centers.

Artificial Intelligence used in future networks is vulnerable to biases, misclassifications, and security threats, which seeds constant scrutiny in accountability. Explainable AI (XAI) methods bridge this gap in identifying unaccounted biases in black-box AI/ML models. However, scaffolding attacks can hide the internal biases of the model from XAI methods, jeopardizing any auditory or monitoring processes, service provisions, security systems, regulators, auditors, and end-users in future networking paradigms, including Intent-Based Networking (IBN). For the first time ever, we formalize and demonstrate a framework on how an attacker would adopt scaffoldings to deceive the security auditors in Network Intrusion Detection Systems (NIDS). Furthermore, we propose a detection method that auditors can use to detect the attack efficiently. We rigorously test the attack and detection methods using the NSL-KDD. We then simulate the attack on 5G network data. Our simulation illustrates that the attack adoption method is successful, and the detection method can identify an affected model with extremely high confidence.

The recent 5G networks aim to provide higher speed, lower latency, and greater capacity; therefore, compared to the previous mobile networks, more advanced and intelligent network security is essential for 5G networks. To detect unknown and evolving 5G network intrusions, this paper presents an artificial intelligence (AI)-based network threat detection system to perform data labeling, data filtering, data preprocessing, and data learning for 5G network flow and security event data. The performance evaluations are first conducted on two well-known datasets-NSL-KDD and CICIDS 2017; then, the practical testing of proposed system is performed in 5G industrial IoT environments. To demonstrate detection against network threats in real 5G environments, this study utilizes the 5G model factory, which is downscaled to a real smart factory that comprises a number of 5G industrial IoT-based devices.

The integration of IoT with cellular wireless networks is expected to deepen as cellular technology progresses from 5G to 6G, enabling enhanced connectivity and data exchange capabilities. However, this evolution raises security concerns, including data breaches, unauthorized access, and increased exposure to cyber threats. The complexity of 6G networks may introduce new vulnerabilities, highlighting the need for robust security measures to safeguard sensitive information and user privacy. Addressing these challenges is critical for 5G networks massively IoT-connected systems as well as any new ones that that will potentially work in the 6G environment. Artificial Intelligence is expected to play a vital role in the operation and management of 6G networks. Because of the complex interaction of IoT and 6G networks, Explainable Artificial Intelligence (AI) is expected to emerge as an important tool for enhancing security. This study presents an AI-powered security system for the Internet of Things (IoT), utilizing XGBoost, Shapley Additive, and Local Interpretable Model-agnostic explanation methods, applied to the CICIoT 2023 dataset. These explanations empowers administrators to deploy more resilient security measures tailored to address specific threats and vulnerabilities, improving overall system security against cyber threats and attacks.

Artificial Intelligence used in future networks is vulnerable to biases, misclassifications, and security threats, which seeds constant scrutiny in accountability. Explainable AI (XAI) methods bridge this gap in identifying unaccounted biases in black-box AI/ML models. However, scaffolding attacks can hide the internal biases of the model from XAI methods, jeopardizing any auditory or monitoring processes, service provisions, security systems, regulators, auditors, and end-users in future networking paradigms, including Intent-Based Networking (IBN). For the first time ever, we formalize and demonstrate a framework on how an attacker would adopt scaffoldings to deceive the security auditors in Network Intrusion Detection Systems (NIDS). Furthermore, we propose a detection method that auditors can use to detect the attack efficiently. We rigorously test the attack and detection methods using the NSL-KDD. We then simulate the attack on 5G network data. Our simulation illustrates that the attack adoption method is successful, and the detection method can identify an affected model with extremely high confidence.

As a result of this new computer design, edge computing can process data rapidly and effectively near to the source, avoiding network resource and latency constraints. By shifting computing power to the network edge, edge computing decreases the load on cloud services centers while also reducing the time required for users to input data. Edge computing advantages for data-intensive services, in particular, could be obscured if access latency becomes a bottleneck. Edge computing raises a number of challenges, such as security concerns, data incompleteness, and a hefty up-front and ongoing expense. There is now a shift in the worldwide mobile communications sector toward 5G technology. This unprecedented attention to edge computing has come about because 5G is one of the primary entry technologies for large-scale deployment. Edge computing privacy has been a major concern since the technology’s inception, limiting its adoption and advancement. As the capabilities of edge computing have evolved, so have the security issues that have arisen as a result of these developments, as well as the increasing public demand for privacy protection. The lack of trust amongst IoT devices is exacerbated by the inherent security concerns and assaults that plague IoT edge devices. A cognitive trust management system is proposed to reduce this malicious activity by maintaining the confidence of an appliance \& managing the service level belief \& Quality of Service (QoS). Improved packet delivery ratio and jitter in cognitive trust management systems based on QoS parameters show promise for spotting potentially harmful edge nodes in computing networks at the edge.

Mobile malware is a malicious code specifically designed to target mobile devices to perform multiple types of fraud. The number of attacks reported each day is increasing constantly and is causing an impact not only at the end-user level but also at the network operator level. Malware like FluBot contributes to identity theft and data loss but also enables remote Command & Control (C2) operations, which can instrument infected devices to conduct Distributed Denial of Service (DDoS) attacks. Current mobile device-installed solutions are not effective, as the end user can ignore security warnings or install malicious software. This article designs and evaluates MONDEO-Tactics5G - a multistage botnet detection mechanism that does not require software installation on end-user devices, together with tactics for 5G network operators to manage infected devices. We conducted an evaluation that demonstrates high accuracy in detecting FluBot malware, and in the different adaptation strategies to reduce the risk of DDoS while minimising the impact on the clients satisfaction by avoiding disrupting established sessions.

The introductory part of the research mainly focuses on the importance of using block chain facilities by using the 5G Network that can be useful for data privacy and security. It can be said that the research mainly focuses on all the benefits of using block chain technology in order to protect all the access of relevant data by implementing intelligent contracts for enhancing the security framework related to the use of 5G networks on the data protection activities. The Literature review of the research mainly concentrates on the benefits and merits of applying the block chain facilities for enhancing both the growth as well as the development of data protection and data privacy. All the merits, as well as demerits of using the block chain facility, have been also discussed throughout the overall research paper. On the other hand, various methods, as well as strategies for applying the block chain facilities, also have been analyzed throughout the literature review section of this research paper. A survey was conducted in this particular scenario to get a clear comprehension of the situation. A survey was conducted with fifty one random people that enable the researches to get a clear picture of the trend while fetching some real life data in this particular scenario.

This paper offers a thorough investigation into quantum cryptography, a security paradigm based on the principles of quantum mechanics that provides exceptional guarantees for communication and information protection. The study covers the fundamental principles of quantum cryptography, mathematical modelling, practical applications, and future prospects. It discusses the representation of quantum states, quantum operations, and quantum measurements, emphasising their significance in mathematical modelling. The paper showcases the real-world applications of quantum cryptography in secure communication networks, financial systems, government and defence sectors, and data centres. Furthermore, it identifies emerging domains such as IoT, 5G networks, blockchain technology, and cloud computing as promising areas for implementing quantum cryptographic solutions. The paper also presents avenues for further research, including post-quantum cryptography, quantum cryptanalysis, multi-party quantum communication, and device-independent quantum cryptography. Lastly, it underscores the importance of developing robust infrastructure, establishing standards, and ensuring interoperability to facilitate widespread adoption of quantum cryptography. This comprehensive exploration of quantum cryptography contributes to the advancement of secure communication, information protection, and the future of information security in the era of quantum technology.

With the advances in 5G communication and mobile device, internet of drones (IoD) has emerged as a fascinating new concept in the realm of smart cities, and has garnered significant interest from both scientific and industrial communities. However, IoD are fragile to variety of security attacks because an adversary can reuse, delete, insert, intercept or block the transmitted messages over an open channel. Therefore, it is imperative to have robust and efficient authentication and key agreement (AKA) schemes for IoD in order to to fulfill the necessary security requirements. Recently, Nikooghadm et al. designed a secure and lightweight AKA scheme for internet of drones (IoD) in IoT environments. However, we prove that their scheme is not resilient to various security threats and does not provide the necessary security properties. Thus, we propose the essential security requirements and guidelines to enhance the security flaws of Nikooghadm et al.’s scheme.

The power communication network based on 5G network slicing is an important foundation to support smart grid, and the bearing of small granularity power regulation and control class services depends on the slicing soft isolation technology, and the data isolation between each soft isolation channel is crucial. In this paper, we propose a new symmetric cryptographic algorithm based on random coding, and establish a hybrid encryption method based on this symmetric algorithm, combined with SM2 and SM3 algorithms, which is suitable for encrypting the data of power regulation and control services. It is also verified through simulation that the proposed hybrid encryption method has high encryption efficiency while ensuring security.

One of the important characteristics envisioned for 6G is security function virtualization (SFV). Similar to network function virtualization (NFV) in 5G networks, SFV provides new opportunities for improving security while reducing the security overhead. In particular, it provides an attractive way of solving compatibility issues related to security. Malware in Internet of Things (IoT) systems is gaining popularity among cyber-criminals because of the expected number of IoT devices in 5G and 6G networks. To solve this issue, this article proposes a security framework that exploits softwarization of security functions via SFV to improve trust in IoT systems and contain the propagation of malware. IoT devices are categorized into trusted, vulnerable, and compromised levels using remote attestation. To isolate the devices in the three distinct categories, NFV is used to create separate networks for each category, and a distributed ledger is used to store the state of each device. Virtualized remote attestation routines are employed to avoid any compatibility issues among heterogeneous IoT devices and effectively contain malware propagation. The results show that the proposed framework can reduce the number of infected devices by 66 percent in only 10 seconds.

Advances in wireless networking, such as 5G, continue to enable the vision of the Internet of Things (IoT), where everything is connected, and much data is collected by IoT devices and made available to interested parties (i.e., application servers). However, events such as botnet attacks (e.g., [1]) demonstrate that there are important challenges in this evolution.

One of the important characteristics envisioned for 6G is security function virtualization (SFV). Similar to network function virtualization (NFV) in 5G networks, SFV provides new opportunities for improving security while reducing the security overhead. In particular, it provides an attractive way of solving compatibility issues related to security. Malware in Internet of Things (IoT) systems is gaining popularity among cyber-criminals because of the expected number of IoT devices in 5G and 6G networks. To solve this issue, this article proposes a security framework that exploits softwarization of security functions via SFV to improve trust in IoT systems and contain the propagation of malware. IoT devices are categorized into trusted, vulnerable, and compromised levels using remote attestation. To isolate the devices in the three distinct categories, NFV is used to create separate networks for each category, and a distributed ledger is used to store the state of each device. Virtualized remote attestation routines are employed to avoid any compatibility issues among heterogeneous IoT devices and effectively contain malware propagation. The results show that the proposed framework can reduce the number of infected devices by 66 percent in only 10 seconds.

Advances in wireless networking, such as 5G, continue to enable the vision of the Internet of Things (IoT), where everything is connected, and much data is collected by IoT devices and made available to interested parties (i.e., application servers). However, events such as botnet attacks (e.g., [1]) demonstrate that there are important challenges in this evolution.

Recently, the manufacturing industry is changing into a smart manufacturing era with the development of 5G, artificial intelligence, and cloud computing technologies. As a result, Operational Technology (OT), which controls and operates factories, has been digitized and used together with Information Technology (IT). Security is indispensable in the smart manu-facturing industry as a problem with equipment, facilities, and operations in charge of manufacturing can cause factory shutdown or damage. In particular, security is required in smart factories because they implement automation in the manufacturing industry by monitoring the surrounding environment and collecting meaningful information through Industrial IoT (IIoT). Therefore, in this paper, IIoT security proposed in 2022 and recent technology trends are analyzed and explained in order to understand the current status of IIoT security technology in a smart factory environment.

Deploying Connected and Automated Vehicles (CAVs) on top of 5G and Beyond networks (5GB) makes them vulnerable to increasing vectors of security and privacy attacks. In this context, a wide range of advanced machine/deep learningbased solutions have been designed to accurately detect security attacks. Specifically, supervised learning techniques have been widely applied to train attack detection models. However, the main limitation of such solutions is their inability to detect attacks different from those seen during the training phase, or new attacks, also called zero-day attacks. Moreover, training the detection model requires significant data collection and labeling, which increases the communication overhead, and raises privacy concerns. To address the aforementioned limits, we propose in this paper a novel detection mechanism that leverages the ability of the deep auto-encoder method to detect attacks relying only on the benign network traffic pattern. Using federated learning, the proposed intrusion detection system can be trained with large and diverse benign network traffic, while preserving the CAVs’ privacy, and minimizing the communication overhead. The in-depth experiment on a recent network traffic dataset shows that the proposed system achieved a high detection rate while minimizing the false positive rate, and the detection delay.

This work presents a modified AlN/Sapphire layered SAW structure localized partial removal of AlN thin film and sapphire, respectively. The SAW propagation and resonance characteristics of the proposed structure with periodic grooves and voids are analyzed using finite element method (FEM). Compared with conventional AlN-based SAW, the proposed structure with optimization configuration and parameters effectively improves the K2 while maintaining a high V, meanwhile eliminates spurious modes. It is demonstrated that the Sezawa mode on the proposed SAW resonator structure offers operating frequencies above 5GHz, K2 values above 6.5\%, and an excellent impedance ratio of 98dB, which makes it a potential candidate for advanced 5G applications.

The continuously growing importance of today’s technology paradigms such as the Internet of Things (IoT) and the new 5G/6G standard open up unique features and opportunities for smart systems and communication devices. Famous examples are edge computing and network slicing. Generational technology upgrades provide unprecedented data rates and processing power. At the same time, these new platforms must address the growing security and privacy requirements of future smart systems. This poses two main challenges concerning the digital processing hardware. First, we need to provide integrated trustworthiness covering hardware, runtime, and the operating system. Whereas integrated means that the hardware must be the basis to support secure runtime and operating system needs under very strict latency constraints. Second, applications of smart systems cover a wide range of requirements where "one- chip-fits-all" cannot be the cost and energy effective way forward. Therefore, we need to be able to provide a scalable hardware solution to cover differing needs in terms of processing resource requirements.In this paper, we discuss our research on an integrated design of a secure and scalable hardware platform including a runtime and an operating system. The architecture is built out of composable and preferably simple components that are isolated by default. This allows for the integration of third-party hardware/software without compromising the trusted computing base. The platform approach improves system security and provides a viable basis for trustworthy communication devices.

The 5G technology ensures reliable and affordable broadband access worldwide, increases user mobility, and assures reliable and affordable connectivity of a wide range of electronic devices such as the Internet of Things (IoT).SDN (Software Defined Networking), NFV ( Network Function Virtualization), and cloud computing are three technologies that every technology provider or technology enabler tries to incorporate into their products to capitalize on the useability of the 5th generation.The emergence of 5G networks and services expands the range of security threats and leads to many challenges in terms of user privacy and security. The purpose of this research paper is to define the security challenges and threats associated with implementing this technology, particularly those affecting user privacy. This research paper will discuss some solutions related to the challenges that occur when implementing 5G, and also will provide some guidance for further development and implementation of a secure 5G system.