Sandeep Pirbhulal

Cognitive Digital Twins (CDTs) are virtual models of physical systems that integrate cognitive functions with real-time Internet of Things (IoT) data to support simulation, decision-making, and cyber resilience. In domains such as healthcare and smart cities, CDTs enable advanced capabilities but also introduce significant privacy and security risks, especially due to continuous, real-time data exchange and automated decision-making. This paper presents DC-TWIN, a dynamic consent-enabled CDT framework that addresses the limitations of static, one-time consent models in real-time, data-intensive environments. DC-TWIN introduces a multi-layered architecture consisting of: (i) a multi-layered architectural stack, including user control, policy management, dynamic trust, and data governance layers, that supports compliance monitoring, risk-aware user interfaces, consent history tracking, federated identity and role binding, and adaptive trust modeling, and (ii) a dynamic consent reasoning engine that uses contextual signals (e.g., user role, device status, network activity) and human factors (e.g., cognitive load, trust calibration, fatigue) to assess data access requests in real time, issuing granular decisions (grant, deny, prompt) or escalating for clarification. We highlight key use cases in healthcare, welfare technologies, and smart cities. The framework empowers users with real-time, contextual control over how their data is accessed, shared, and reused through adaptive interfaces and personalized consent mechanisms. By integrating dynamic consent reasoning, trust calibration, and continuous feedback loops, DC-TWIN supports transparent, compliant, and user-aligned data governance. It contributes to the secure and ethical deployment of CDTs by reinforcing user autonomy, enhancing risk communication, and enabling responsive consent management in critical domains such as healthcare.

The cyberattack surface in critical sectors is expanding due to the rapid proliferation of Internet of Things (IoT) devices. Artificial Intelligence (AI) models, such as Deep Neural Networks (DNNs) and Convolutional Neural Networks (CNNs), offer promising capabilities for detecting and classifying cyber threats. However, these models often struggle to generalize to previously unseen attacks after deployment. This study investigates how well different AI techniques can generalize to such novel threats in the presence of class imbalance. We evaluate three data balancing strategies: Generative Adversarial Networks (GAN), Synthetic Minority Over-sampling Technique (SMOTE), and class weighting. Experimental results indicate that DNNs outperform CNNs when provided with identical input data. While each balancing method has distinct advantages and trade-offs, the highest multiclass accuracy of 81.16 % was achieved by a DNN using GAN-augmented data for the previously seen attack types. The best performance on unseen attacks was achieved by a DNN trained with SMOTE, yielding a multiclass accuracy of 51 % among eight classes. The binary classification (benign vs. malicious) results were satisfactory, with DNN using GAN-augmented data achieving an accuracy of 99.20 %. These findings highlight the importance of not only separating data into training and test splits, but also incorporating a “seen vs. unseen” evaluation strategy.

Industrial Control Systems (ICSs), which are essential components of critical infrastructures, are inherently complex and vulnerable to cyberattacks. Advanced Persistent Threats (APTs) that target these systems are multi-stage, coordinated attacks that can lead not only to information loss but also to physical damage and loss of life. Traditional threat modeling approaches fall short in adapting to the dynamic nature of ICSs, necessitating new methodologies to predict and prevent such complex attacks. This work presents a digital twin-assisted dynamic threat modeling framework for ICS environments. The framework leverages a knowledge graph that integrates system data and cyber threat intelligence to predict potential attacks. In addition, the digital twin environment enables the validation of mitigation strategies before deployment in the physical system, while also supporting adaptive response and real-time mitigation. To predict the attacker’s next move, we propose a Relational Graph Convolutional Network (RGCN)-based model that utilizes enriched relational data such as tactics, campaigns, groups, techniques, and assets. The proposed RGCN model achieves a recall of 0.887, an F1-score of 0.893, and an AUC of 0.957 in predicting potential attack sequences. These results demonstrate that the model provides reliable and well-balanced predictive performance.

Recognizing Urdu text in natural images is more challenging as compared to other languages, such as English, due to the cursive nature of Urdu script. However, Urdu scene text has not received enough attention from both industry and academia due to the lack of the dataset of Urdu text. We propose a large-scale Urdu Scene Text Dataset (USTD) to address this problem, which is designed for Urdu scene text detection and recognition. The proposed dataset contains 29674 text annotations (17877 Urdu and 11797 English), 749725 characters in 6389 images. It covers a wide variety of text images with both Nastaleeq and Naskh writing styles, taken from different streets and roads of Pakistan. The vast diversity of this dataset makes it a benchmark to work on and train robust neural networks for the detection and recognition of cursive text. Besides, baseline results are also provided with several state-of-the-art networks, including TextBoxes++, Seglink, DB(ResNet-50) and EAST for text localization and Convolutional Recurrent Neural Network (CRNN) for text recognition. To further evaluate the performance of these models, we have used the most popular evaluation matrices of precision, recall, and F-measure. Our experimental outputs reveal that an end-to-end combination of DB(ResNet-50) and CRNN provides the best results with precision, recall, and F-measure of 0.7526, 0.5974, and 0.6660, respectively.

Health systems in Norway and across Europe are under increasing strain from chronic conditions, long waiting times, and limited clinical capacity. At the same time, evidence-based digital therapeutics (DTx) are emerging as regulated tools to prevent, manage, and treat disease through clinically validated software interventions. Scandinavia has played a pioneering role in evaluating internet-delivered cognitive behavioural therapy for conditions such as insomnia and perinatal depression, demonstrating that well-designed, integrated digital solutions can complement existing healthcare services. However, despite promising trial evidence, large-scale adoption of DTx remains inconsistent due to regulatory, reimbursement, organisational, and cultural barriers. In parallel, simulation-based methods such as discrete-event modelling and digital twins are increasingly applied to optimise healthcare delivery and test alternative service configurations. Building on these developments, this project proposes the creation of an adaptive digital twin of a chronic care pathway to analyse how different deployment strategies for prescription digital therapeutics could impact system access, resilience, and resource utilisation.

The evolution of 5G networks toward 6G presents an opportunity to shift from centralized security to decentralized, intelligent, and autonomous security. While 5G introduces decentralization through software-based virtualization and edge computing, it also exposes network infrastructure to a new and dynamic threat landscape that still relies largely on static, centrally deployed threat detection. This work proposes a lightweight anomaly detection framework based on Tiny Machine Learning (TinyML), specifically designed to address the latency, memory, and energy constraints of 5G edge devices. Using promising model compression techniques, including integer quantization and pruning, we demonstrate that highly optimized models can run entirely on-device without relying on cloud infrastructure. Our experiments using a real-world 5G dataset demonstrate that the quantized TinyDenseNet achieves over 99% detection accuracy while keeping the model size below 30 KB and inference latency under 11 ms. By embedding intelligent detection at the network edge, this approach presents self-defending, ultra-reliable, and context-aware infrastructures for future 6G networks. The proposed approach focuses on next-generation networks, in which decentralized, low-power, and privacy-preserving security mechanisms will be essential.

Federated decentralized IoT systems are reshaping how data is exchanged and processed across domains such as smart cities and healthcare, yet ensuring trust in such dynamic, distributed environments remains a significant challenge. This paper introduces FedTrust, a layered framework that integrates decentralized communication, federated data services, and a self-adaptive trust-risk model to assess the trustworthiness of IoT devices in real time. By extending an established analytical trust model, we refine existing constructs and introduce new dimensions such as context awareness and behavioral monitoring to account for the operational variability of edge devices. Our proposed model computes risk and trust scores using weighted metrics, driving automated decisions and mitigation actions via a closed feedback loop. FedTrust provides a scalable and resilient approach for securing federated IoT ecosystems through continuous, data-driven trust calibration.

Advancements in Deep Neural Network (DNN) models and hardware accelerators have made edge intelligence a practical alternative to cloud-based intelligence. However, application-specific requirements, such as accuracy, latency, security, and privacy, as well as workload fluctuations, necessitate dynamic allocation of edge and cloud resources. To facilitate such dynamic allocation, we propose an adaptive model selection and switching framework that leverages operational performance scores. We evaluate the approach using various object classification models, demonstrating its ability to balance accuracy and inference time while ensuring scalability and efficient resource utilization.

In the context of a 5G-IoT communication environment, devices and users interact through the Internet, exposing them to numerous anomalies arising from diverse cybersecurity challenges including DDoS attacks, malware, and man-in-the-middle attacks. Consequently, it is essential to develop robust anomaly detection (AD) solutions specifically designed for 5G-IoT applications to safeguard them against these cyber threats. In this study, we propose an AI-based AD system that utilizes real-time traffic data for 5G-IoT networks, designed specifically for critical sectors such as healthcare, smart grid, industry 4.0, etc. The proposed AD system comprises three synchronized components aimed at enhancing the cybersecurity of 5G-enabled systems. These components include the 5G-IoT communication infrastructure, an AI-enabled AD engine, and an anomaly dashboard. To evaluate the performance of the proposed system, we conducted experiments using two AI models: Graph Neural Network (GNN) and Convolutional Neural Network (CNN). These experiments were performed on real-time 5G data, which included both benign and malicious traffic, generated over a 5G wireless network. The 5G-IoT anomaly detection system was evaluated using feature subsets, for k = 10 and k = 25. The results showed that with k = 10 and using the GNN learning model, the overall accuracy achieved was 99.19%. For the benign case, the precision was 98.86%, while for the malicious case, the precision was even higher at 99.71%. From our analysis, it can be concluded that the proposed system using GNN demonstrates promising results for binary classification in real-time 5G-IoT anomaly detection.

CybAlliance is dedicated to fostering excellence in both research and education within the realms of cybersecurity and privacy in the healthcare sectors of Norway, the USA, France, and Germany. To fulfill this mission, a key objective is to facilitate the national and international mobility of students, researchers, and staff members. This objective is executed under Work Package 2 (WP2), titled "Research Cooperation," specifically Task 2.1, "Mobility." The aim of Task 2.1 is to organize 24 mobility placements for students, researchers, and staff at partner institutions. This report details the outcomes of Deliverable 2.1, the "Annual Mobility Report," for the activities carried out in the year 2025. It encompasses the planning, execution, and outcomes of the mobility stays, analyzing their impact on enhancing collaborative research and educational endeavors among the partner institutions.

The primary objective of CybAlliance is to establish a long-term strategic alliance to enhance research and education in cybersecurity and privacy within the healthcare sector. To achieve this, one of the key objectives is to organize workshops, webinars, and open seminars, facilitating discussions among national and international collaborators about the strengths and opportunities within this domain. This objective is pursued under WP2 ("Research Cooperation"), specifically Task 2.2 ("Organize the Annual Workshop/Webinar"), which aims to hold annual workshops and webinars. These events bring together like-minded researchers and stakeholders from various regions to discuss domain-specific strengths and opportunities. Additionally, the creation of various blogs and social media articles aims to broaden awareness of potential developments to a larger audience. This task not only provides networking opportunities and a platform for disseminating results in education, research, and innovation but also contributes to developing INTPART synergies and collaborations in healthcare projects. This report details the outcomes of D2.2 ("Organize the Annual National Workshop") carried out in 2025.

CybAlliance is dedicated to enhancing healthcare security and privacy competencies by fostering international collaboration in research, innovation, and education. A key objective to support this goal is the organization of workshops, webinars, and open seminars, enabling both national and international collaborators to convene and explore the strengths and opportunities within the domain. This objective is encapsulated within WP2 ("Research Cooperation"), specifically under Task 2.3 ("Open Seminar"), which seeks to provide a platform for discussing the outcomes of the project with interested industry and academic personnel who are not part of the consortium. Through this task, the project offers networking opportunities and a platform for disseminating results related to educational, research, and innovative activities, by organizing both national and international academic and industrial open seminars. This report details the outcomes of D2.3 ("Organize Open Seminar") accomplished in 2025, highlighting the achievements and impact of this task.

Modern healthcare infrastructures and services are dependent on advanced data analytics, sensing and communication technologies that include 5G/6G networks, Artificial intelligence (AI), Internet of Medical Things (IoMT), Information Technology (IT), and Operational Technology (OT). This integration incurs a large vulnerability surface and cyber attackers can exploit those vulnerabilities to wage successful attacks against modern healthcare infrastructures and services. Therefore, identifying potential vulnerabilities in healthcare infrastructures and services and securing end-to-end monitoring of sensitive healthcare infrastructures and services are crucial for achieving resilient healthcare infrastructures and services. In this paper, we propose an architecture designed to enhance the resilience of healthcare infrastructures and services. This architecture is centered around the concept of Adaptive Cognitive Digital Twins (ACDTs), which are capable of orchestrating adaptive defenses to proactively respond to anticipated cyber attacks. Our architecture has seven layers. We detail the functions at each layer of the architecture to guide the design and development of mechanisms that can be employed at each layer.

SUNRISE2024isaforumforresearchersandpractitionersworkingonthesecureandresilientdigitaltransformationofhealthcare.Digitaltransformationinhealthcareencom-passestheuseofadvancedtechnologiestoenhancepatientcareandaddresstheevolvingdemandsofcaredelivery,particularlythetransitiontohome-basedcarefromhospitalsettings.Whilecenteringonpatientneeds,italsoentailsindispensableadjustmentsandadvancementsofhealthcareprocesses.Whereasvariousbenefitsofthistransforma-tionarebroadlyacknowledged,theincreasedconnectivity,thehugevolumeofsensitivehealthinformation,andthelackofsufficientcybersecurityawarenessandcultureamongbothhealthcareprofessionalsandpatientsresultinincreasedcybersecurityriskandmakedigitalhealthcareattractivetocybercriminalsandpronetocybersecurityattackssuchasphishing,ransomware,distributeddenial-of-serviceattacks,andmalware.Thecon-nectionofmedicaldevicestotheInternet,hospitalnetworks,andotherdevicesextendsthepotentialforattacks,therebyraisingconcernsforpatientsafety.TheCOVID-19pandemicbroughtattentiontotheinterconnectednatureofcybersecurityandprivacyrisksinhealthcare.Theneedtoenhancecybersecurityandresilienceinhealthcareanditssupplychainhasbeenheightened,requiringthedevelopmentofnewsolutions.Toaddressthesechallenges,theworkshopaimedtobringtogethersecurityresearchersandpractitioners,healthcareprofessionalsandmanagersofhealthcaretorethinksecuredigitalizationandresilienceofhealthcare.

The lecture is based on the article https://hdl.handle.net/11250/5326972

Advancements in Deep Neural Network (DNN) models and hardware accelerators have made edge intelligence a practical alternative to cloud-based intelligence. However, application specific requirements, such as accuracy, latency, security, and privacy, as well as workload fluctuations, necessitate dynamic allocation of edge and cloud resources. To facilitate such dynamic allocation, we propose an adaptive model selection and switching framework that leverages operational performance scores. We evaluate the approach using various object classification models, demonstrating its ability to balance accuracy and inference time while ensuring scalability and efficient resource utilization.

Critical Infrastructures, such as healthcare, are essential for maintaining societal well-being and bolstering the nation's economy. The growing integration of Cyber Physical Systems (CPSs), such as social robots, into these infrastructures has made them more susceptible to both random faults and cyber-attacks. Traditional risk assessment frameworks typically address either safety or security risks, but often lack the capability to dynamically assess and mitigate both in an integrated manner. In our previous work, we developed a Bayesian Network (BN) framework that helps in developing BN models for distinguishing random faults and attacks, primarily for diagnostic purposes. However, this framework did not include proactive security measures. In this study, we enhance the BN framework to facilitate the development of models that incorporate proactive security measures by considering mitigating factors. In addition, we introduce extended Component Fault Trees (CFTs) for knowledge elicitation, leveraging their formal structure and practitioners’ familiarity with Fault Tree analysis. We propose a translation scheme from extended CFTs to BNs to further refine the framework. The effectiveness of this framework is demonstrated through two use cases: remote patient monitoring in healthcare, and the deployment of social robots in smart cities. This study presents a holistic framework for dynamic safety and security risk assessment in critical environments, featuring a closed feedback loop between information sources and the risk evaluation and treatment stages to ensure continuous monitoring, analysis, and adaptation to evolving risks.

Information and communication technology (ICT) has brought about a profound transformation in numerous facets of contemporary society, spanning education, communication, healthcare, commerce, governance, and banking. However, it has resulted in a significant rise in cyber-attacks, often with disastrous consequences. Hence, it becomes imperative to guarantee the security of the information and computing system. Security assurance provides confidence by ensuring that the security features, practices, procedures, and architectural elements of software systems serve as effective mediators and enforcers of the security policy while also demonstrating resilience against security failures and attacks. In the past, several quantitative security assurance methods have been proposed. The majority of these methods rely on the security requirements and/or threat profiles to qualify the security level of the system based on an interview with the owner and development team. While conducting an interview, the quality of data we receive often depends on the interviewer’s ability, and interviewers sometimes face a dilemma when answering a question. A security testing team also faces the same challenges during the testing and enumeration. In this paper, we have proposed a fuzzy-based security assurance approach to model the uncertainty generated by these factors in decision-making. This approach will be helpful in scenarios where security professionals and testing teams are uncertain about a statement or situation. The proposed method is implemented on a private cloud infrastructure based on OpenStack.

The advancements in communication technology (5G and beyond) and global connectivity Internet of Things (IoT) also come with new security problems that will need to be addressed in the next few years. The threats and vulnerabilities introduced by AI/ML based 5G and beyond IoT systems need to be investigated to avoid the amplification of attack vectors on AI/ML. AI/ML techniques are playing a vital role in numerous applications of cybersecurity. Despite the ongoing success, there are significant challenges in ensuring the trustworthiness of AI/ML systems. However, further research is needed to define what is considered an AI/ML threat and how it differs from threats to traditional systems, as currently there is no common understanding of what constitutes an attack on AI/ML based systems, nor how it might be created, hosted and propagated [ETSI, 2020]. Therefore, there is a need for studying the AI/ML approach to ensure safe and secure development, deployment, and operation of AI/ML based 5G and beyond IoT systems. For 5G and beyond, it is essential to continuously monitor and analyze any changing environment in real-time to identify and reduce intentional and unintentional risks. In this study, we will review the role of the AI/ML technique for 5G and beyond security. Furthermore, we will provide our perspective for predicting and mitigating 5G and beyond security using AI/ML techniques

A compelling webinar that delves deep into the transformative impact of Adaptive AI on the healthcare industry. Discover how this cutting-edge technology is not only reshaping patient care, diagnostics, and treatment plans but also revolutionizing administrative processes by enhancing accuracy and improving outcomes. Learn about the real-time adaptive learning capabilities of AI and its role in personalizing medicine, optimizing operational efficiency, enhancing security, and setting new standards in healthcare excellence. This session is a part of the ENFIELD project, a pioneering initiative to establish a European Center of Excellence for AI, focusing on its application in healthcare, one of the key verticals of the project. Understand how Adaptive AI, as one of the core pillars of ENFIELD, ensures trustworthiness, sustainability, and enhanced security while driving advancements in healthcare. Ideal for healthcare professionals, AI researchers, policymakers, and tech enthusiasts, this webinar offers a unique opportunity to gain insights into the future of healthcare, shaped by ethical, sustainable, and highly effective AI solutions. Don’t miss the chance to see how Adaptive AI is pioneering secure healthcare innovation, promising transformative potential for Europe and beyond.

This report presents the progress and achievements of Work Package WP4, encompassing three primary components: Experience Sharing (WP.4.1), Project Proposal Ideas (WP.4.2), and Joint Publications (WP.4.3). Significant strides have been made in 2024 in advancing healthcare cybersecurity and adaptive technologies, including webinars on cutting-edge topics, innovative project proposals, and impactful research publications. Looking ahead, we aim to expand our project proposals to European-wide initiatives.

The 5th generation (5G) and beyond use Internet of Things (IoT) to offer the feature of remote monitoring for different applications such as transportation, healthcare, and energy. There are several advantages of 5G and beyond for IoT applications like high speed and low latency. However, they are prone to cybersecurity threats due to networks softwarization and virtualization, thus raising additional security challenges and complexities. In this paper, we conducted a systematic literature review (SLR) of cybersecurity for 5G and beyond-enabled IoT. By developing a taxonomy to classify and characterize existing research, we identified and analyzed strategies, key patterns, mechanisms, performance evaluation, validation parameters and challenges of cybersecurity and resilience for 5G and beyond-enabled IoT in existing studies. We used “Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)” recommendations for this SLR. Through our search in scientific databases, 4449 records published between 2017 and 2023 were initially identified, which were then reduced to 558 records after title and abstract screening to be considered for the eligibility check process. After screening the full-text, 79 articles were finalized for thorough analysis. The findings of this study suggest that 35% of the included studies focus on authentication and access control as security aspects, 59% studies are based on combination of both network layer and application layer as main operation layer, and 34% of the included studies use real-time implementation for validation purpose while the remaining studies utilize simulation or theoretical analysis. Our SLR also highlights open research challenges of 5G and beyond-enabled IoT cybersecurity and suggests a tentative solution for each challenge, which can be a focus of future research. Finally, key limitations of our SLR and threats to validity are addressed.

Digital Twins (DTs), serving as virtual replicas of physical systems, facilitate novel pathways for real-time monitoring, and informed decision making in different healthcare applications such as remote surgery, hospital management, and telemedicine. In the rapidly evolving landscape of cyber security, the emergence of DTs has provided unparalleled capabilities of preempting cyber threats, testing incident response strategies, and compliance testing. Moreover, Cognitive Digital Twins (CDTs) not only replicate physical systems but also have the ability to learn and make decisions. However, such a human-in-the-loop decision making approach is lacking for improving security in Information Technology (IT) and Operational Technology (OT) infrastructures while IT-OT integration in healthcare introduces new cyber security concerns and an increasing threat landscape. In this study, we developed a conceptual CDT-based adaptive cyber security framework for IT-OT enabled healthcare applications which has the potential to address cyber threats in varying situations. This framework integrates physical and virtual healthcare twin for healthcare service providers in addition to a knowledge base of security/privacy events and cognitive cycle for facilitating the human-in-the-loop approach. This framework could enhance cyber security in IT-OT healthcare by incorporating interdisciplinary fields such as adaptive security, health information exchange, human factors, IT-OT integration, risk management, among others. This study also presents some prominent use cases for IT-OT healthcare systems

Home Sensing Technology Conference paper A Cognitive Digital Twin Architecture for Cybersecurity in IoT-Based Smart Homes Sandeep Pirbhulal, Habtamu Abie, Ankur Shukla & Basel Katt Conference paper First Online: 09 April 2023 336 Accesses Part of the Lecture Notes in Electrical Engineering book series (LNEE,volume 1035) Abstract Cognitive Digital Twin (CDT) is an extension of Digital Twin with cognitive capabilities to monitor and analyse complex and unforeseen behaviours and to ensure critical reasoning and decision-making. Thus, CDT has a potential for enhancing cybersecurity for the Internet of Things (IoT)-based applications such as smart homes. In this paper, we developed a conceptual CDT architecture for improving cybersecurity in smart homes with dynamic threat detection and mitigation capabilities. The proposed approach applies closed feedback loops between cognitive process and cybersecurity using artificial intelligence and machine learning techniques. This will allow continuous monitoring of security-related information and analytics with complex behaviours within a virtual environment. The developed approach allows security testing and simulation in the virtual world for prediction and anticipation of dynamic security threats. It also facilitates dynamic updates to the physical world of attack prevention strategies for the dynamic optimization of smart homes security. Finally, this paper discusses the applicability of the developed CDT architecture in other IoT-based critical sectors.

Nowadays, microservices-based applications such as E-Business, E-Healthcare, 3D-Gaming, and Augmented Reality has latterly drawn attention in the research area. The microservices enabled applications are different from traditional monolithic applications with the high demand of security and fault detection, therefore, a lightweight secure and failure detection enabled schemes widely required for the new applications. This paper proposes a new lightweight microservices mobile cloud (Mob-Cloud) the framework that replaces the heavyweight virtual machine (VM) based on mobile cloud computing (MCC). The study devises MFHE (Modified Fully Homomorphism Encryption) and WATFA (Workload Assignment Transient Fault Aware) schemes to deal with security and failures. Simulation results show that the proposals are practical for the considered problem

Nowadays, microservices-based applications such as E-Business, E-Healthcare, 3D-Gaming, and Augmented Reality have latterly drawn attention in the research area. The microservices enabled applications are different from traditional monolithic applications with high demand of security and fault detection, therefore, a lightweight secure and failure detection enabled schemes widely required for the new applications. This paper proposes a new lightweight microservices mobile cloud (Mob-Cloud) framework that replaces the heavyweight virtual machine (VM) based on mobile cloud computing (MCC). The study devises MFHE (Modified Fully Homomorphism Encryption) and WATFA (Workload Assignment Transient Fault Aware) schemes to deal with security and failures. Simulation results show that the proposals are practical for the considered problem.

In this paper, we address the problem of compatibility between digital signature schemes and in-network aggregation approaches. In the IoT world, the gateways alter the signed network flows when performing in-network aggregation. Therefore, existing conventional approaches are not suitable for verifying the authenticity of the original flows. This raises the need for energy-effective and secure schemes that enable the destination to validate aggregated network flows. In this regard, a lightweight homomorphic signature scheme is proposed which supports the implementation of aggregation procedures without affecting the verification process. We demonstrate the unforgeability and the privacy of our scheme. We also perform an analytical study of its energy-efficiency. The results suggest that the proposed scheme considerably decreases the processing overhead of the existing set-homomorphic signature schemes. Moreover, it does not add any communication overhead to traditional (non-homomorphic) signature schemes. This, in turn, improves the energy consumption by 30% compared to existing homomorphic signature techniques.

In recent years, the cybersecurity attacks on the internet of things (IoT)-based healthcare systems became the major concern for researchers and health organizations. With time, the sophistication of these attacks is increasing. Therefore, healthcare service providers must implement efficient security mechanisms carefully while taking the advantages of connected devices without compromising the patient safety and disturbing the real-time health services. A digital twin (DT) is a virtual representation of a real-time counterpart of a physical world. DT offers significant advantages to cybersecurity experts, empowering them to predict risks without entering the physical world, and to simulate and test cyber-attacks that would otherwise be infeasible to do in real-time in the physical environment. DT in healthcare helps to identify security vulnerabilities, conduct attack simulations, and potential security breaches by creating a virtual replica of the targeted healthcare systems. In this paper, a novel and automated conceptual framework is developed for reinforcing the cybersecurity in IoT-based healthcare using DT technology. It includes the conceptualization and analysis of the proposed framework which can provide dynamic and adaptive security solution to identify real-time threats and vulnerabilities in IoTbased healthcare applications.

In recent times, security and safety are, at least, conducted in safety-sensitive or critical sectors. Nevertheless, both processes do not commonly analyze the impact of security risks on safety. Several scholars are focused on integrating safety and security risk assessments, using different methodologies and tools in critical infrastructures (CIs). Bayesian networks (BN) and graph theory (GT) have received much attention from academia and industries to incorporate security and safety features for different CI applications. Hence, this study aims to conduct a systematic literature review (SLR) for co-engineering safety and security using BN or GT. In this SLR, the preferred reporting items for systematic reviews and meta-analyses recommendations (PRISMA) are followed. Initially, 2295 records (acquired between 2011 and 2020) were identified for screening purposes. Later on, 240 articles were processed to check eligibility criteria. Overall, this study includes 64 papers, after examining the pre-defined criteria and guidelines. Further, the included studies were compared, regarding the number of required nodes for system development, applied data sources, research outcomes, threat actors, performance verification mechanisms, implementation scenarios, applicability and functionality, application sectors, advantages, and disadvantages for combining safety, and security measures, based on GT and BN. The findings of this SLR suggest that BN and GT are used widely for risk and failure management in several domains. The highly focused sectors include studies of the maritime industry (14%), vehicle transportation (13%), railway (13%), nuclear (6%), chemical industry (6%), gas and pipelines (5%), smart grid (5%), network security (5%), air transportation (3%), public sector (3%), and cyber-physical systems (3%). It is also observed that 80% of the included studies use BN models to incorporate safety and security concerns, whereas 15% and 5% for GT approaches and joint GT and BN methodologies, respectively. Additionally, 31% of identified studies verified that the developed approaches used real-time implementation, whereas simulation or preliminary analysis were presented for the remaining methods. Finally, the main research limitations, concluding remarks and future research directions, are presented

These days, healthcare applications on the Internet of Medical Things (IoMT) network have been growing to deal with different diseases via different sensors. These healthcare sensors are connecting to the various healthcare fog servers. The hospitals are geographically distributed and offer different services to the patients from any ubiquitous network. However, due to the full offloading of data to the insecure servers, two main challenges exist in the IoMT network. (i) Data security of workflows healthcare applications between different fog healthcare nodes. (ii) The cost-efficient and QoS efficient scheduling of healthcare applications in the IoMT system. This paper devises the Cost-Efficient Service Selection and Execution and Blockchain-Enabled Serverless Network for Internet of Medical Things system. The goal is to choose cost-efficient services and schedule all tasks based on their QoS and minimum execution cost. Simulation results show that the proposed outperform all existing schemes regarding data security, validation by 10%, and cost of application execution by 33% in IoMT.