Next-Gen Security: Leveraging Advanced Technologies for Social Medical Public Healthcare Resilience

The healthcare industry is undergoing a significant change as it incorporates advanced technologies to strengthen its security infrastructure and improve its ability to withstand current challenges and  explores the important overlap between security, technology, and public health. The introductory section presents a thorough overview, highlighting the current status of public healthcare and emphasizing the crucial importance of security in protecting confidential medical data. This statement highlights the current difficulties encountered by social medical public healthcare systems and emphasizes the urgent need to utilize advanced technologies to strengthen their ability to adapt and recover. The systematic literature review explores a wide range of studies, providing insight into the various aspects of healthcare security. This text examines conventional security methods, exposes their constraints, and advances the discussion by examining cutting-edge technologies such as Artificial Intelligence (AI), Machine Learning, Blockchain, Internet of Things (IoT), and Biometric Security Solutions. Every technology is carefully examined to determine its ability to strengthen healthcare systems against cyber threats and breaches, guaranteeing the confidentiality and accuracy of patient data. The methodology section provides a clear explanation of the research design, the process of selecting participants, and the strategies used for analyzing the data. The research seeks to evaluate the present security situation and determine the best methods for incorporating advanced technologies into healthcare systems, using either qualitative or quantitative methods. The following sections elucidate the security challenges inherent in social medical public healthcare, encompassing cyber threats and privacy concerns. Drawing on case studies, the paper illustrates successful implementations of advanced technologies in healthcare security, distilling valuable lessons and best practices. The recommendations section goes beyond the technical domain, exploring the policy implications and strategies for technological implementation. The exploration of regulatory frameworks, legal considerations, and ethical dimensions is conducted to provide guidance for the smooth integration of advanced technologies into healthcare systems. Healthcare professionals are encouraged to participate in training and awareness programs to ensure a comprehensive and efficient implementation. To summarize, the paper combines the results, highlighting the importance of utilizing advanced technologies to strengthen the security framework of social medical public healthcare. The significance of healthcare resilience is emphasized, and potential areas for future research are delineated. This research is an important resource that offers valuable insights and guidance for stakeholders, policymakers, and technologists who are dealing with the intricate field of healthcare security in the age of advanced technologies. DOI: https://doi.org/10.52710/seejph.48

Securing the Digital Frontier: The Role of Technology in Social Medical Public Healthcare Security

The rapid expansion of digital connectivity within social medical public healthcare systems (SMPH) has fundamentally transformed the way patient care is delivered. However, it has also made sensitive data vulnerable to a wide range of cybersecurity threats. This study introduces and assesses a new hybrid deep learning model, GANA-AO, with the aim of improving real-time anomaly detection and threat prevention in SMPH. GANA-AO leverages the capabilities of Generative Adversarial Networks (GAN) and Autoencoders, enhanced by Adam optimization, to achieve outstanding accuracy and generalizability. Generative Adversarial Networks (GAN) produce authentic artificial data to supplement the training dataset and tackle the problem of imbalanced classes. On the other hand, Autoencoders acquire compact representations of normal data, aiding in the detection of anomalies by identifying deviations. Adam optimization effectively adjusts model hyperparameters, thereby improving performance. The efficacy of GANA-AO is demonstrated through our experiments conducted on the publicly accessible IoT-23 dataset. The model demonstrates an exceptional accuracy of 98.33% and a True Positive Rate (TPR) of 98.67%, surpassing the performance of baseline models by a significant margin. The results emphasize the capability of GANA-AO to enhance SMPH cybersecurity by promptly detecting and addressing malicious activities, protecting sensitive healthcare data, and ensuring patient safety. This paper not only introduces a robust technical solution but also highlights the vital significance of technology in safeguarding the digital boundaries of SMPH. By adopting cutting-edge approaches such as GANA-AO, we can establish a stronger and more adaptable system, promoting confidence and enabling patients in the digital era of healthcare. DOI: https://doi.org/10.52710/seejph.48

Cyber-Physical Systems and Smart Cities in India: Opportunities, Issues, and Challenges

A large section of the population around the globe is migrating towards urban settlements. Nations are working toward smart city projects to provide a better wellbeing for the inhabitants. Cyber-physical systems are at the core of the smart city setups. They are used in almost every system component within a smart city ecosystem. This paper attempts to discuss the key components and issues involved in transforming conventional cities into smart cities with a special focus on cyber-physical systems in the Indian context. The paper primarily focuses on the infrastructural facilities and technical knowhow to smartly convert classical cities that were built haphazardly due to overpopulation and ill planning into smart cities. It further discusses cyber-physical systems as a core component of smart city setups, highlighting the related security issues. The opportunities for businesses, governments, inhabitants, and other stakeholders in a smart city ecosystem in the Indian context are also discussed. Finally, it highlights the issues and challenges concerning technical, financial, and other social and infrastructural bottlenecks in the way of realizing smart city concepts along with future research directions

Digital twins in cyber effects modelling of IoT/CPS points of low resilience

The exponential increase of data volume and velocity have necessitated a tighter linkage of physical and cyber components in modern Cyber–physical systems (CPS) to achieve faster response times and autonomous component reconfiguration. To attain this degree of efficiency, the integration of virtual and physical components reinforced by artificial intelligence also promises to improve the resilience of these systems against organised and often skillful adversaries. The ability to visualise, validate, and illustrate the benefits of this integration, while taking into account improvements in cyber modelling and simulation tools and procedures, is critical to that adoption. Using Cyber Modelling and Simulation (M&S) this study evaluates the scale and complexity required to achieve an acceptable level of cyber resilience testing in an IoT-enabled critical national infrastructure (CNI). This research focuses on the benefits and challenges of integrating cyber modelling and simulation (M&S) with digital twins and threat source characterisation methodologies towards a cost-effective security and resilience assessment. Using our dedicated DT environment, we show how adversaries can utilise cyber–physical systems as a point of entry to a broader network in a scenario where they are trying to attack a port

Digital twins in cyber effects modelling of IoT/CPS points of low resilience

The exponential increase of data volume and velocity have necessitated a tighter linkage of physical and cyber components in modern Cyber–physical systems (CPS) to achieve faster response times and autonomous component reconfiguration. To attain this degree of efficiency, the integration of virtual and physical components reinforced by artificial intelligence also promises to improve the resilience of these systems against organised and often skillful adversaries. The ability to visualise, validate, and illustrate the benefits of this integration, while taking into account improvements in cyber modelling and simulation tools and procedures, is critical to that adoption. Using Cyber Modelling and Simulation (M&S) this study evaluates the scale and complexity required to achieve an acceptable level of cyber resilience testing in an IoT-enabled critical national infrastructure (CNI). This research focuses on the benefits and challenges of integrating cyber modelling and simulation (M&S) with digital twins and threat source characterisation methodologies towards a cost-effective security and resilience assessment. Using our dedicated DT environment, we show how adversaries can utilise cyber–physical systems as a point of entry to a broader network in a scenario where they are trying to attack a port

Explainable AI over the Internet of Things (IoT): Overview, State-of-the-Art and Future Directions

Explainable Artificial Intelligence (XAI) is transforming the field of Artificial Intelligence (AI) by enhancing the trust of end-users in machines. As the number of connected devices keeps on growing, the Internet of Things (IoT) market needs to be trustworthy for the end-users. However, existing literature still lacks a systematic and comprehensive survey work on the use of XAI for IoT. To bridge this lacking, in this paper, we address the XAI frameworks with a focus on their characteristics and support for IoT. We illustrate the widely-used XAI services for IoT applications, such as security enhancement, Internet of Medical Things (IoMT), Industrial IoT (IIoT), and Internet of City Things (IoCT). We also suggest the implementation choice of XAI models over IoT systems in these applications with appropriate examples and summarize the key inferences for future works. Moreover, we present the cutting-edge development in edge XAI structures and the support of sixth-generation (6G) communication services for IoT applications, along with key inferences. In a nutshell, this paper constitutes the first holistic compilation on the development of XAI-based frameworks tailored for the demands of future IoT use cases.Comment: 29 pages, 7 figures, 2 tables. IEEE Open Journal of the Communications Society (2022

Innovation in manufacturing through digital technologies and applications: Thoughts and Reflections on Industry 4.0

The rapid pace of developments in digital technologies offers many opportunities to increase the efficiency, flexibility and sophistication of manufacturing processes; including the potential for easier customisation, lower volumes and rapid changeover of products within the same manufacturing cell or line. A number of initiatives on this theme have been proposed around the world to support national industries under names such as Industry 4.0 (Industrie 4.0 in Germany, Made-in-China in China and Made Smarter in the UK). This book presents an overview of the state of art and upcoming developments in digital technologies pertaining to manufacturing. The starting point is an introduction on Industry 4.0 and its potential for enhancing the manufacturing process. Later on moving to the design of smart (that is digitally driven) business processes which are going to rely on sensing of all relevant parameters, gathering, storing and processing the data from these sensors, using computing power and intelligence at the most appropriate points in the digital workflow including application of edge computing and parallel processing. A key component of this workflow is the application of Artificial Intelligence and particularly techniques in Machine Learning to derive actionable information from this data; be it real-time automated responses such as actuating transducers or informing human operators to follow specified standard operating procedures or providing management data for operational and strategic planning. Further consideration also needs to be given to the properties and behaviours of particular machines that are controlled and materials that are transformed during the manufacturing process and this is sometimes referred to as Operational Technology (OT) as opposed to IT. The digital capture of these properties and behaviours can then be used to define so-called Cyber Physical Systems. Given the power of these digital technologies it is of paramount importance that they operate safely and are not vulnerable to malicious interference. Industry 4.0 brings unprecedented cybersecurity challenges to manufacturing and the overall industrial sector and the case is made here that new codes of practice are needed for the combined Information Technology and Operational Technology worlds, but with a framework that should be native to Industry 4.0. Current computing technologies are also able to go in other directions than supporting the digital ‘sense to action’ process described above. One of these is to use digital technologies to enhance the ability of the human operators who are still essential within the manufacturing process. One such technology, that has recently become accessible for widespread adoption, is Augmented Reality, providing operators with real-time additional information in situ with the machines that they interact with in their workspace in a hands-free mode. Finally, two linked chapters discuss the specific application of digital technologies to High Pressure Die Casting (HDPC) of Magnesium components. Optimizing the HPDC process is a key task for increasing productivity and reducing defective parts and the first chapter provides an overview of the HPDC process with attention to the most common defects and their sources. It does this by first looking at real-time process control mechanisms, understanding the various process variables and assessing their impact on the end product quality. This understanding drives the choice of sensing methods and the associated smart digital workflow to allow real-time control and mitigation of variation in the identified variables. Also, data from this workflow can be captured and used for the design of optimised dies and associated processes