Search

Implementing 5G-enabled Vehicle-to-Everything (V2X) intelligent transportation systems presents a promising opportunity to enhance road safety and traffic flow while facilitating the integration of artificial intelligence (AI) based solutions. Yet, security and privacy concerns pose significant challenges that must be addressed. Therefore, researchers have focused on improving the security and integrity of vehicle data sharing, with a particular emphasis on V2X application layer security and privacy requirements. This is crucial given that V2X networks can consist of vehicles manufactured by different companies and registered in various jurisdictions, which may only be within communication range for a few seconds. Thus, it is necessary to establish a trusting relationship between vehicles quickly. The article proposes a threshold cryptography-based key exchange protocol that meets the key requirements for V2X data sharing and privacy, including the rapid establishment of trust, the maintenance of vehicle anonymity, and the provision of secure messages. To evaluate the feasibility and performance of the proposed protocol, a tailored testbed that leverages the NS-3 network simulator, a commercial 5G network, and public cloud infrastructure is used. Overall, the proposed protocol provides a potential solution for addressing security and privacy concerns in V2X networks, which is essential for successfully implementing and adopting this technology.

Technology has become an integral part of our educational systems, and its importance in our schools cannot be overstated. However, digital skills, unlike other literacy skills, such as reading, writing, and numeracy, still have many discontinuities between how children use them at home versus in school. Therefore, in Wales (UK), digital skills are being promoted as part of the Digital Competence Framework (DCF) and feature highly in the new Curriculum for Wales (2022). Moreover, the new Digital Technology General Certificate of Secondary Education (GCSE) in Wales has been introduced to provide learners with the opportunity to gain a qualification that builds digital skills, knowledge, and understanding. However, this also brings many challenges for teachers, such as a lack of confidence, knowledge, and training, as well as a lack of resources and fear of change, to name a few. These challenges, in turn, have an impact on pupils’ motivation and performance, as well as parents’ ability to support their children. This paper presents a qualitative case study on the development of a new digital technology learning community for primary and secondary school pupils, their teachers, and parents in Blaenau Gwent, Wales (UK). Firstly, the paper will provide insight into what was required to establish an effective learning community, including ensuring engagement and buy-in from all stakeholders. Secondly, through the description, analysis, and interpretation of findings from two studies, the paper will highlight the impact of the DTLSN learning community on teachers and pupils in Blaenau Gwent, especially in terms of their learning and teaching.

Software security vulnerabilities are significant for the software development industry. Exploration is conducted for software development industry landscape, software development eco-system landscape, and software system customer landscape. The focus is to explore the data sources that can provide the software development team with insights to act upon the security vulnerabilities proactively. Across these modules of software landscape, customer landscape, and industry landscape, data sources are leveraged using artificial intelligence approaches to identify the security insights. The focus is also on building a smart knowledge management system that integrates the information processed across modules into a central system. This central intelligence system can be further leveraged to manage software development activities proactively. In this exploration, machine learning and deep learning approaches are devised to model the data and learn from across the modules. Architecture for all the modules and their integration is also proposed. Work helps to envision a smart system for Artificial Intelligence-based knowledge management for managing software security vulnerabilities.

The malware can threaten personal privacy by opening backdoors for attackers to access user passwords, IP addresses, banking information, and other personal data, whilst some malware extracts personal data and sends them to people unknown to the users. In this chapter, the authors will present recent case studies and discuss the privacy and security threats associated with different types of malwares. The small medium enterprises (SMEs) have a unique working model forming the backbone of the UK economy and malware affects SMEs’ organizations. Also, the use of Artificial Intelligence (AI) as both an offense and defense mechanism, for the hacker, and the end user will be investigated further. In conclusion, finding a balance between IT expertise and the costs of products that are able to help SMEs protect and secure their data will benefit the SMEs by using a more intelligent controlled environment with applied machine learning techniques and not compromising on costs will be discussed.

Unreliable networks often use excess bandwidth for data integration in smart cities. For this purpose, Messaging Queuing Telemetry Transport (MQTT) with a certain quality of service (QoS) is employed. Data integrity and data security are frequently compromised for reducing bandwidth usage while designing integrated applications. Thus, for a reliable and secure integrated Internet of Everything (IoE) service, a range of network parameters are conditioned to achieve the required quality of a deliverable service. In this work, a QoS-0-based MQTT is developed in such a manner that the transparent MQTT protocol uses Transmission Control Protocol (TCP)-based connectivity with various rules for the retransmission of contents if the requests are not entertained for a fixed duration. The work explores the ways to improve the overall content delivery probability. The parameters are examined over a transparent gateway-based TCP network after developing a mathematical model for the proposed retransmission-based mutant QoS-0. The probability model is then verified by an actual physical network where the repeated content delivery is explored at VM-based MQTT, local network-based broker and a remote server. The results show that the repeated transmission of contents from the sender improves the content delivery probability over the unreliable MQTT-based Internet of Things (IoT) for developing smart cities’ applications.

In-vehicle communication has become an integral part of today’s driving environment considering the growing add-ons of sensor-centric communication and computing devices inside a vehicle for a range of purposes including vehicle monitoring, physical wiring reduction, and driving efficiency. However, related literature on cyber security for in-vehicle communication systems is still lacking potential dedicated solutions for in-vehicle cyber risks. Existing solutions are mainly relying on protocol-specific security techniques and lacking an overall security framework for in-vehicle communication. In this context, this paper critically explores the literature on cyber security for in-vehicle communication focusing on technical architecture, methodologies, challenges, and possible solutions. In-vehicle communication network architecture is presented considering key components, interfaces, and related technologies. The protocols for in-vehicle communication have been classified based on their characteristics, and usage type. Security solutions for in-vehicle communication have been critically reviewed considering machine learning, cryptography, and port-centric techniques. A multi-layer secure framework is also developed as a protocol and use case-independent in-vehicle communication solution. Finally, open challenges and future dimensions of research for in-vehicle communication cyber security are highlighted as observations and recommendations.

The increased dependency on technology brings national security to the forefront of concerns of the 21st century. It creates many challenges to developing and developed nations in their effort to counter cyber threats and adds to the inherent risk factors associated with technology. The failure to securely protect data would potentially give rise to far-reaching catastrophic consequences. Therefore, it is crucially important to have national, regional, and global data protection policies and regulations to penalise those engaged in unethical use of technology and abuse the system vulnerabilities of technology. This research paper aims to analyse GDPR inspired Bills in the South Asian Region and to identify their appropriateness for developing a global level data protection mechanism, given that Asian nations are far more diverse than those of the European nations. Against that background, the objectives of this paper are to identify GDPR inspired Bills in the South Asian Region, identify the similarities and disparities, and the barriers to developing a regional level data protection mechanism, thereby fulfilling the need for developing a global level mechanism. This research is qualitative in nature, and with that in mind, the researcher conducted an extensive literature survey of previous research papers, journal articles, previous survey reports and government publications on the above content. Taking account of the findings of the survey, the researcher critically analysed the important parameters identified in the literature review. The key findings of this research indicate that many countries in the South Asian region are in the process of reviewing their current data protection mechanisms, in line with GDPR. In concluding, the researcher emphasised the need to develop adequate data protection mechanisms and believed that going forward it would be the appropriate and practical way to develop a consensus-based regional mechanism that would ultimately enable to develop a lasting global level data protection mechanism.

Technology has become an integral part of our educational systems, and its importance in our schools cannot be overstated. However, digital skills, unlike other literacy skills, such as reading, writing, and numeracy, still have many discontinuities between how children use them at home versus in school. Therefore, in Wales (UK), digital skills are being promoted as part of the Digital Competence Framework (DCF) and feature highly in the new Curriculum for Wales (2022). Moreover, the new Digital Technology General Certificate of Secondary Education (GCSE) in Wales has been introduced to provide learners with the opportunity to gain a qualification that builds digital skills, knowledge, and understanding. However, this also brings many challenges for teachers, such as a lack of confidence, knowledge, and training, as well as a lack of resources and fear of change, to name a few. These challenges, in turn, have an impact on pupils’ motivation and performance, as well as parents’ ability to support their children. This paper presents a qualitative case study on the development of a new digital technology learning community for primary and secondary school pupils, their teachers, and parents in Blaenau Gwent, Wales (UK). Firstly, the paper will provide insight into what was required to establish an effective learning community, including ensuring engagement and buy-in from all stakeholders. Secondly, through the description, analysis, and interpretation of findings from two studies, the paper will highlight the impact of the DTLSN learning community on teachers and pupils in Blaenau Gwent, especially in terms of their learning and teaching.

Artificial intelligence (AI) is a field of computer science that deals with the simulation of human intelligence using machines so that such machines gain problem-solving and decision-making capabilities similar to that of the human brain. Neuroscience is the scientific study of the struczture and cognitive functions of the brain. Neuroscience and AI are mutually interrelated. These two fields help each other in their advancements. The theory of neuroscience has brought many distinct improvisations into the AI field. The biological neural network has led to the realization of complex deep neural network architectures that are used to develop versatile applications, such as text processing, speech recognition, object detection, etc. Additionally, neuroscience helps to validate the existing AI-based models. Reinforcement learning in humans and animals has inspired computer scientists to develop algorithms for reinforcement learning in artificial systems, which enables those systems to learn complex strategies without explicit instruction. Such learning helps in building complex applications, like robot-based surgery, autonomous vehicles, gaming applications, etc. In turn, with its ability to intelligently analyze complex data and extract hidden patterns, AI fits as a perfect choice for analyzing neuroscience data that are very complex. Large-scale AI-based simulations help neuroscientists test their hypotheses. Through an interface with the brain, an AI-based system can extract the brain signals and commands that are generated according to the signals. These commands are fed into devices, such as a robotic arm, which helps in the movement of paralyzed muscles or other human parts. AI has several use cases in analyzing neuroimaging data and reducing the workload of radiologists. The study of neuroscience helps in the early detection and diagnosis of neurological disorders. In the same way, AI can effectively be applied to the prediction and detection of neurological disorders. Thus, in this paper, a scoping review has been carried out on the mutual relationship between AI and neuroscience, emphasizing the convergence between AI and neuroscience in order to detect and predict various neurological disorders.

By leveraging ubiquitous computing and the Internet of Things (IoT), smart cities gain potential to provide a wider range of services. Different homogeneous and heterogeneous networking schemes and applications have been proposed in the literature to date. In these networking schemes, human and computer are connected for social, economic, physiological, and technological growth. However, there is a dearth of recent literature that incorporates recently proposed and operating techniques and technologies capable of enhancing the productivity of human and machine in IoT technologies. The role of this research is to investigate the protocols, followed by the advance frameworks for IoT, the characteristics and services that are being governed using IoT for establishing information-rich smart cities. To this end, likewise, physical layer, media access control, networking and applications protocols, and encapsulation standards of IoT for smart cities applications are critically reviewed. Certain open issues are discussed based on the literature collected that would improve the autonomous behavior, process control, device handling, and the QoS in smart environments.

In recent years, due to the rapid growth in network technology, numerous types of intrusions have been uncovered that differ from the existing ones, and the conventional firewalls with specific rule sets and policies are incapable of identifying those intrusions in real-time. Therefore, that demands the requirement of a real-time intrusion detection system (RT-IDS). The ultimate purpose of this research is to construct an RT-IDS capable of identifying intrusions by analysing the inbound and outbound network data in real-time. The proposed system consists of a deep neural network (DNN) trained using 28 features of the NSL-KDD dataset. In addition, it contains the machine learning (ML) pipeline with sequential components for categorical data encoding and feature scaling, which is used before transmitting the real-time data to the trained DNN model to make predictions. Moreover, a real-time feature extractor, which is a C++ program that sniffs data from the real-time network traffic and derives relevant data related to the features of the NSL-KDD dataset using the sniffed data, is deployed between the gateway router and the local area network (LAN). Together with the trained DNN model, the ML pipeline is hosted in a server that can be accessed via a representational state transfer application programming interface (REST API). The DNN has revealed outstanding testing performance results achieving 81%, 96%, 70% and 81% for accuracy, precision, recall and f1-score accordingly. This research comprises a comprehensive technical explanation concerning the implementation and functionality of the complete system. Moreover, leveraging the extensive explanations provided in this paper, advanced IDSs capable of identifying modern intrusions can be constructed.

Threat modelling Cyber-Physical System built on cloud infrastructure to monitor and manage the window cleaning operation using Window Cleaning Warehouse as a case study. Focusing on IoT data collection and cloud infrastructure security and the connections with the Cyber-Physical System. External dependencies and trust levels are defined before using trust boundaries and data flow diagrams to highlight attack surfaces. Expected scenarios from the data flow diagrams are discussed to identify violated intended use of the system using STRIDE threat classification. A risk assessment of assets that may be of interest to an adversary aid the discovery of more security risks that are then prioritised using the DREAD methodology. The results of the research present a comprehensive breakdown of vulnerabilities associated with IoT data security for route optimisation ranging from GPS spoofing, to Firestore vulnerabilities in the real-time database to Bluetooth Low Energy vulnerabilities in the IoT hardware, all of which could be common risks in cyber-physical systems designed by SME businesses. The research concludes various security risks applicable to SME businesses adopting industry 4.0 to alleviate the risk of new security breaches to the business through this adoption, increasing the likelihood of successful adoption of industry 4.0.

In this paper, we identify and review key challenges to bridge the knowledge-gap between SME’s, companies, organisations, businesses, government institutions and the general public in adopting, promoting and utilising Blockchain technology. The challenges indicated are Cybersecurity and Data privacy in this instance. Additional challenges are set out supported by literature, in researching data security management systems and legal frameworks to ascertaining the types and varieties of valid encryption, data acquisition, policy and outcomes under ISO 27001 and the General Data Protection Regulations. Blockchain, a revolutionary method of storage and immutability, provides a robust storage strategy, and when coupled with a Smart Contract, gives users the ability to form partnerships, share information and consent via a legally-based system of carrying out business transactions in a secure digital domain. Globally, ethical and legal challenges significantly differ; consent and trust in the public and private sectors in deploying such defensive data management strategies, is directly related to the accountability and transparency systems in place to deliver certainty and justice. Therefore, investment and research in these areas is crucial to establishing a dialogue between nations to include health, finance and market strategies that should encompass all levels of society. A framework is proposed with elements to include Big Data, Machine Learning and Visualisation methods and techniques. Through the literature we identify a system necessary in carrying out experiments to detect, capture, process and store data. This includes isolating packet data to inform levels of Cybersecurity and privacy-related activities, and ensuring transparency demonstrated in a secure, smart and effective manner.

Artificial Intelligence (Al) models are being produced and used to solve a variety of current and future business and technical problems. Therefore, AI model engineering processes, platforms, and products are acquiring special significance across industry verticals. For achieving deeper automation, the number of data features being used while generating highly promising and productive AI models is numerous, and hence the resulting AI models are bulky. Such heavyweight models consume a lot of computation, storage, networking, and energy resources. On the other side, increasingly, AI models are being deployed in IoT devices to ensure real-time knowledge discovery and dissemination. Real-time insights are of paramount importance in producing and releasing real-time and intelligent services and applications. Thus, edge intelligence through on-device data processing has laid down a stimulating foundation for real-time intelligent enterprises and environments. With these emerging requirements, the focus turned towards unearthing competent and cognitive techniques for maximally compressing huge AI models without sacrificing AI model performance. Therefore, AI researchers have come up with a number of powerful optimization techniques and tools to optimize AI models. This paper is to dig deep and describe all kinds of model optimization at different levels and layers. Having learned the optimization methods, this work has highlighted the importance of having an enabling AI model optimization framework.

The emergence of Industry 4.0, Industry IoT (IIoT), Internet of Everything (IoE), Internet of Medical Things (IoMT) and Cyber Physical Systems (CPS) have created a paradigm shift in the way we design, develop, operate and manage engineering systems. At present, most of the legacy and modern engineering systems/applications are connected via Internet or private networks to provide an efficient service as well as to manage the system productively (e.g., automation).

The cloud computing model has brought in a bevy of digital innovations and disruptions. With the faster proliferation of containers, which are being positioned as the most optimal runtime for microservices-centric applications, the usage of container orchestration platform solutions (alternatively container life-cycle management platforms) has recently gone up significantly. Therefore, establishing, sustaining and managing containerized cloud environments is getting a lot of interest among worldwide cloud experts and data center pioneers.

The Window Cleaning Operation in recent years has transformed from a “bucket & ladder” operation using tap-water to a high-tech industry utilising pure water and specialised van systems. The Covid-19 pandemic hereto invites the onset of more frequent cleaning and numerous window cleaning startups which is both opportunity and a challenge to shape-up an innovative growth for managing the next normal for the Window Cleaning Operation.

The growing dependency on digital technologies is becoming a way of life, and at the same time, the collection of data using them for surveillance operations has raised concerns. Notably, some countries use digital surveillance technologies for tracking and monitoring individuals and populations to prevent the transmission of the new coronavirus. The technology has the capacity to contribute towards tackling the pandemic effectively, but the success also comes at the expense of privacy rights. The crucial point to make is regardless of who uses and which mechanism, in one way another will infringe personal privacy. Therefore, when considering the use of technologies to combat the pandemic, the focus should also be on the impact of facial recognition cameras, police surveillance drones, and other digital surveillance devices on the privacy rights of those under surveillance. The GDPR was established to ensure that information could be shared without causing any infringement on personal data and businesses; therefore, in generating Big Data, it is important to ensure that the information is securely collected, processed, transmitted, stored, and accessed in accordance with established rules. This paper focuses on Big Data challenges associated with surveillance methods used within the COVID-19 parameters. The aim of this research is to propose practical solutions to Big Data challenges associated with COVID-19 pandemic surveillance approaches. To that end, the researcher will identify the surveillance measures being used by countries in different regions, the sensitivity of generated data, and the issues associated with the collection of large volumes of data and finally propose feasible solutions to protect the privacy rights of the people, during the post-COVID-19 era.

Data Cleaning: Challenges and Novel Solutions