Multi-Partner Project: CyberSecDome - Framework for Secure, Collaborative, and Privacy-Aware Incident Handling for Digital Infrastructure

Human error has been statistically proven to be the primary cause of road accidents. This undoubtedly is a contributory cause of the rising popularity of autonomous vehicles as they are presumably able to maneuver appropriately/optimally on the roads while diminishing the likelihood of human error and its repercussion. However, autonomous vehicles are not ready for widespread adoption because their safety and security issues are yet to be thoroughly investigated/addressed. Little literature could be found on collaborative analysis of safety and security of autonomous vehicles. This paper proposes a framework for analyzing both safety and security issues, which includes an integrated safety and security method (S&S) with international vehicle safety and security standards ISO 26262 and SAE J3061. The applicability of the proposed framework is demonstrated using an example of typical autonomous vehicle model. Using this framework, one can clearly understand the vehicle functions, structure, the associated failures and attacks, and also see the vulnerabilities that are not yet addressed by countermeasures, which helps to improve the in-vehicle safety and security from researching and engineering perspectives.

With the wide application of cloud computing in multi-tenant, heterogeneous nodes and high-concurrency environments, model parameters frequently interact during distributed training, which easily leads to privacy leakage, communication redundancy, and decreased aggregation efficiency. To realize the collaborative optimization of privacy protection and computing performance, this study proposes the Heterogeneous Federated Homomorphic Encryption Cloud (HFHE-Cloud) model, which integrates federated learning (FL) and homomorphic encryption and constructs a secure and efficient collaborative learning framework for cloud platforms. Under the condition of not exposing the original data, the model effectively reduces the performance bottleneck caused by encryption calculation and communication delay through hierarchical key mapping and dynamic scheduling mechanism of heterogeneous nodes. The experimental results show that HFHE-Cloud is significantly superior to Federated Averaging (FedAvg), Federated Proximal (FedProx), Federated Personalization (FedPer) and Federated Normalized Averaging (FedNova) in comprehensive performance, Homomorphically Encrypted Federated Averaging (HE-FedAvg) and other five baseline models. In the dimension of privacy protection, the global accuracy is up to 94.25%, and the Loss is stable within 0.09. In terms of computing performance, the encryption and decryption time is shortened by about one third, and the encryption overhead is controlled at 13%. In terms of distributed training efficiency, the number of communication rounds is reduced by about one fifth, and the node participation rate is stable at over 90%. The results verify the model's ability to achieve high security and high scalability in multi-tenant environment. This study aims to provide cloud service providers and enterprise data holders with a technical solution of high-intensity privacy protection and efficient collaborative training that can be deployed in real cloud platforms.

The global environment is becoming more complex and aggressive, with numerous threats from, undesirable foreign influence, traditional, political and economic espionage, organised crime, bio-security; natural disasters, pandemics, and terrorism. New Zealand has experienced all of these. With this extensive, but by no means exhaustive list, the question must be asked, why then does New Zealand not have a national security strategy, one that focuses the nation’s resources on reducing these threats and risks? A national security strategy is not one that needs to give away all of the state’s secrets, it can simply be a framework for collaboration across the government on national security. It should be the basis for a joined up approach across government to crisis management and conflict. What New Zealand currently has is a collection of unrelated documents with varying levels of connectivity to national security – the only forward looking documents are those from the New Zealand Defence Force. New Zealand needs to adopt a forward looking strategic approach to national security and develop a national security strategy. This strategy needs to align the policies of different government agencies towards a cohesive national security framework that targets our most important threats, so that the harm they cause does not eventuate or is mitigated.

The deployment of Next Generation Networks (NGNs) across Africa is accelerating digital connectivity and economic growth. However, this transformation is accompanied by escalating cybersecurity risks that challenge the effectiveness of traditional perimeter-based security architectures. This paper presents an Artificial Intelligence (AI)-driven Zero Trust Security (ZTS) framework specifically designed for NGN environments within the African context. Rooted in the "never trust, always verify" principle, the proposed framework leverages machine learning and behavioral analytics to enable continuous authentication, dynamic access control, and real-time threat mitigation. The integration of AI enhances the system’s ability to detect anomalous activities, respond to zero-day attacks, and adapt security policies based on contextual awareness. Recognizing Africa’s unique challenges—including infrastructural constraints, diverse regulatory landscapes, and limited skilled cybersecurity personnel, the model emphasizes scalability, automation, and cost-efficiency. Simulations and case studies demonstrate the proposed solution’s effectiveness in mitigating insider threats, lateral movement, and advanced persistent threats (APTs) within NGN infrastructures. The findings suggest that AI-powered ZTS can serve as a foundational cybersecurity strategy for building resilient, intelligent, and secure digital ecosystems in Africa. Index Terms: Artificial Intelligence (AI), Zero Trust Security (ZTS), Next Generation Networks (NGN), Cybersecurity, Network Access Control, Behavioral Analytics, Advanced Persistent Threats (APTs), African Telecommunications, Context-Aware Security, Dynamic Threat Detection, Identity and Access Management (IAM), Digital Infrastructure Security, Policy-Based Security.

Against the converging backdrop of China's Rural Revitalization, Digital China, and Strong Sports Nation strategies, rural sports events, represented by the "Village Super League" ("Cun Chao") and "Village BA" ("Cun BA"), have emerged explosively. They demonstrate powerful cultural communication capabilities and industrial derivative potential, with their "IP-ization" (Intellectual Property-ization) becoming a new kinetic force for comprehensive rural revitalization. However, the explosive growth of these phenomenal events is accompanied by severe governance challenges. These include the conflict between cultural authenticity and excessive commercialization, the contradiction between short-term traffic dividends and long-term sustainable development, the interest imbalances among pluralistic actors, and the "technological paradox" introduced by digital technology intervention. Traditional governance models dominated by a single actor are no longer adequate for the complex ecosystem of rural sports IP-ization in the digital age. It is imperative to construct a governance system that is adaptive, inclusive, and resilient. Adhering to a qualitative research paradigm, this paper employs governance theory, digital empowerment theory, and co-evolutionary theory to deeply analyze the internal mechanisms of digital technology empowering the IP-ization of rural sports events. The study posits that digital technology is not merely a communication tool but a fundamental force reshaping the production relations, organizational forms, and value chains of rural sports, fostering a connection between the "glocal" (global + local) value of the IP. However, technological empowerment also triggers governance dilemmas such as the "digital divide," "platform dependency," and "cultural alienation." The study argues that the governance logic for rural sports event IP-ization must fundamentally shift from "singular control" to "pluralistic collaboration," from "empirical decision-making" to "data-driven" governance, and from "rigid regulation" to "resilient good governance." Based on this, the paper constructs a "five-in-one" collaborative governance mechanism framework. This includes: a dynamic mechanism centered on "value co-creation and benefit sharing"; a coordination mechanism keyed on "digital negotiation and platform regulation"; a guarantee mechanism based on "digital infrastructure and digital literacy"; a deviation correction mechanism guided by "culture-centrism and risk prevention"; and a subject-responsibility mechanism that clarifies the roles of the government (helmsman), villagers (protagonists), platforms (connectors), capital (boosters), and professional elites (think tank). This research aims to provide a theoretical reference and practical strategy for the sustainable development of rural sports event IP in the digital age, aspiring to achieve true resonance between technological empowerment and rural cultural revitalization.

Cyberthreats and hazardous application usage have increased due to growing use of internet services and apps over computer networks, endangering customer privacy and service availability. To address this, a hybrid optimized attention residual net security framework for network Intrusion Detection System (IDS) is employed in digital infrastructure to identify abnormal traffic behaviors that conventional firewalls are unable to detect. In IDS, the input data is preprocessed to eliminate noisy and irrelevant data. The raw data is preprocessed by comprising encoding and normalization techniques. It ensures resolution and makes it suitable for the DL model. A novel framework is an attention residual net that focuses on prominent features in the input data and eliminates vanishing gradient issues. The performance of the attention residual net is further enhanced by utilizing the Dragon Fly Optimization (DFO) algorithm that finely tunes its hyperparameters. The hybrid approach is implemented in Python and combines the efficiency of DL-based hybrid Attention ResNet and DFO algorithms to attain a robust and secure IDS, thus making it feasible for safeguarding digital infrastructure.

Abstract.The operationalisation of the Indo-Pacific Regional Information Sharing (IORIS) platform is examined as a tool for advancing maritime governance across one of the world's most strategically significant maritime regions. Funded by the European Union through the CRIMARIO initiative, IORIS facilitates real-time, secure communication among over 100 maritime stakeholders, tackling persistent challenges like piracy, geopolitical instability, regulatory complexity, and environmental risks. Drawing on maritime governance theory, collaborative governance frameworks, and socio-technical systems perspectives, this study applies a qualitative content analysis of policy documents, institutional reports, and scholarly literature. The results highlight IORIS’s role in dismantling institutional silos, enhancing situational awareness, supporting regulatory compliance, and streamlining emergency response. By integrating digital infrastructure and fostering cross-sectoral cooperation, the platform contributes to a more resilient and secure maritime environment. The findings affirm the need for sustained technological innovation, expanded regional partnerships, and adaptive policy frameworks to ensure the long-term stability of global maritime trade.

The escalating complexity of digital threats has transformed the landscape of cybercrime, necessitating robust security frameworks to protect digital infrastructure. Indonesia faces an array of cybersecurity challenges, encompassing unauthorized system penetration, data compromise, digital infrastructure disruption, and malicious software deployment. The management of these risks encompasses systematic approaches to detection, evaluation, mitigation, and ongoing monitoring. Strategic defense initiatives include recruiting specialized digital security professionals to enhance national protection mechanisms and establishing dedicated cybersecurity operations facilities. As digital transformation accelerates, the protection of information assets and computing resources has become paramount. This research examines the multifaceted challenges of digital security, with particular emphasis on methodological approaches, empirical observations, and analytical discourse regarding Indonesia's cybersecurity landscape. The opening segment contextualizes the critical nature of digital defense mechanisms amid rising technological threats. The theoretical framework examines contemporary cybersecurity paradigms and relevant scholarly contributions. The investigation employs qualitative methodologies, utilizing targeted case analyses to thoroughly examine digital security challenges. The investigation reveals that while students demonstrate basic cybersecurity awareness, substantial improvements are needed in Indonesia's broader digital defense capabilities. The study identifies key protective measures against digital threats, including robust authentication protocols, critical information consumption practices, and enhanced security awareness programs. The analysis highlights the intricate nature of combating digital threats and emphasizes the need for strengthened institutional responses coupled with enhanced public understanding of digital safety protocols.

The global stock market has experienced a profound transformation over the past few decades due to rapid advancements in Information Technology (IT). Traditional stock market systems, once dominated by manual trading floors, paper-based documentation, and delayed information dissemination, have been replaced by highly automated, technology-driven platforms. Information Technology now forms the backbone of modern stock market operations, enabling real-time trading, high-speed data processing, electronic clearing and settlement, and enhanced market surveillance. This research paper examines the role of Information Technology in shaping modern stock market practices, with particular emphasis on trading efficiency, transparency, investor participation, decision-making processes, and associated technological risks. The study adopts a descriptive and empirical research design to analyze how IT-enabled systems—such as electronic trading platforms, online brokerage services, mobile trading applications, algorithmic trading systems, and digital data analytics tools—have redefined stock market functioning. Primary data were collected through a structured questionnaire administered to 104 respondents, including individual investors, students, salaried employees, and businesspersons with exposure to stock market activities. The questionnaire was designed to assess awareness levels, usage patterns, perceived benefits, and perceived risks related to IT-based stock trading systems. Secondary data were sourced from academic journals, textbooks, stock exchange publications, and authoritative financial websites to establish a strong theoretical and conceptual foundation. The findings of the study indicate that awareness of Information Technology in the stock market is generally high among respondents; however, the depth of understanding varies considerably. Online trading platforms and mobile trading applications are perceived as the most important technological systems, highlighting the growing importance of accessibility and convenience in modern trading practices. A significant proportion of respondents acknowledge that IT has led to substantial improvements in trading speed and operational efficiency, allowing transactions to be executed within seconds. At the same time, a notable segment of investors perceives electronic trading systems as complex, indicating challenges related to digital literacy and platform usability. The study further reveals mixed perceptions regarding the role of real-time data and digital analytics in investment decision-making. While many respondents believe that technology-based tools support better and faster investment decisions, others associate continuous data flow with confusion, impulsive behavior, or increased risk. Awareness of algorithmic and automated trading exists at a basic level, but detailed understanding remains limited, suggesting a gap between technological advancement and investor knowledge. Cybersecurity threats, system failures, and technical errors emerge as the most significant risks associated with IT-driven stock market systems. The increasing dependence on digital infrastructure has heightened concerns regarding data security, system reliability, and market stability. Nevertheless, technological advancements have also strengthened regulatory oversight through automated surveillance systems, improved transparency, and faster settlement cycles. The study concludes that Information Technology has fundamentally reshaped modern stock market practices by enhancing efficiency, transparency, and inclusivity. However, the effective utilization of technology depends on continuous investor education, simplified system design, robust cyber security frameworks, and adaptive regulatory mechanisms. The paper contributes to existing literature by offering an integrated analysis of technological, behavioral, and operational dimensions of modern stock markets, particularly from the perspective of an emerging economy. It provides practical insights for policymakers, stock exchanges, brokerage firms, and investors seeking to balance technological innovation with market stability and investor protection.

This technical article explores the transformative role of artificial intelligence and machine learning technologies in strengthening financial security frameworks and combating fraudulent activities across digital platforms. As financial transactions increasingly migrate to digital environments, traditional security measures prove inadequate against sophisticated fraud tactics. The article examines how AI-driven solutions leverage behavioral analytics, anomaly detection, and biometric authentication to create adaptive security systems that continuously evolve against emerging threats. By analyzing implementation strategies across major financial networks including credit card processors, mobile banking platforms, and cryptocurrency exchanges, the article provides a comprehensive overview of current applications while highlighting the challenges and opportunities that will shape the future landscape of financial security. Furthermore, the article identifies critical research priorities including quantum-resistant algorithms, federated learning approaches, explainable AI methodologies, and human-AI collaboration frameworks that will define the next generation of financial security systems in an increasingly complex threat environment.

The “One Belt, One Road” (OBOR) initiative, now widely referred to as the Belt and Road Initiative (BRI), represents one of the most ambitious infrastructure and economic development projects in modern history, encompassing over 140 participating countries. Despite its potential for fostering global connectivity and economic growth, OBOR projects face significant risks, including financial, operational, geopolitical, and environmental uncertainties. This study explores the potential of artificial intelligence (AI) to revolutionize risk management in OBOR infrastructure projects, addressing challenges such as cost overruns, project delays, and political instability. By leveraging AI technologies such as machine learning, natural language processing, predictive analytics, and risk assessment models, stakeholders can enhance their ability to identify, quantify, and mitigate risks in real-time. AI tools offer unparalleled capabilities in processing vast amounts of data from multiple sources, including financial reports, satellite imagery, and social media, to predict and analyse risks. For instance, AI-driven algorithms can monitor geopolitical developments to assess the likelihood of conflicts or trade disruptions affecting project timelines. Similarly, predictive models can forecast weather patterns and environmental hazards, enabling project planners to implement proactive strategies for mitigating potential disruptions. This study employs a mixed-methods approach, combining quantitative data analysis and qualitative case studies of OBOR infrastructure projects that have successfully implemented AI-driven risk management solutions. The findings demonstrate that AI significantly enhances decision-making accuracy, improves resource allocation, and reduces the probability of adverse events. Case studies from railway and port construction projects in Southeast Asia and Central Asia illustrate how AI tools have enabled project managers to optimize operations, minimize delays, and reduce costs. However, the study also identifies challenges associated with integrating AI into OBOR projects, including the high cost of technology adoption, the need for skilled professionals, and ethical concerns surrounding data privacy and algorithmic transparency. Moreover, disparities in digital infrastructure and AI readiness among OBOR partner countries pose additional barriers to widespread implementation. These challenges highlight the need for strategic investments in capacity-building and collaborative frameworks to ensure equitable access to AI technologies. The study concludes that AI has the potential to transform risk management practices in OBOR infrastructure projects, fostering greater efficiency, resilience, and sustainability. Policymakers and project stakeholders are encouraged to prioritize AI integration by establishing supportive regulatory environments, incentivizing innovation, and fostering partnerships between technology providers and infrastructure developers. Future research should focus on developing localized AI solutions tailored to the specific needs and contexts of OBOR partner countries, ensuring that the benefits of AI-driven risk management are accessible to all.

Educational institutions are facing increasingly complex cyber threats, particularly as they continue to rely on on-premises servers with limited cybersecurity resources. Zero Trust Architecture (ZTA) offers a security model that rejects implicit trust and requires strict verification of each access request. This study aims to examine the applicability of ZTA in managing local servers within educational institutions through a qualitative literature review approach. Relevant literature from 2014 to 2025 was analyzed using thematic synthesis to identify recurring concepts, strategies, and gaps. The results show that ZTA, when integrated with Identity and Access Management (IAM), Security Information and Event Management (SIEM), and real-time monitoring tools like Zabbix, Prometheus, and Grafana, significantly enhances network security by implementing granular access controls and continuous monitoring. However, the implementation is constrained by limited infrastructure and IT governance maturity in many institutions. This research is considered significant because it addresses a pressing gap in the cybersecurity practices of educational institutions, which often lack the robust frameworks and resources found in the corporate sector. By offering an adaptable and scalable ZTA-based security framework, the study contributes to advancing secure digital infrastructure and data protection in the education domain. Therefore, a strategic and phased adoption is recommended, supported by IT governance frameworks such as COBIT. This study contributes a conceptual framework that links ZTA, server monitoring, and institutional readiness, and offers recommendations for building sustainable cybersecurity strategies in the educational sector.

With the development of Internet of Things communities, the complexity and scale of cyber security threats multiply swiftly in these intelligent cities and their connected digital infrastructure. As hybrid computing and cyber environment continue to develop, NGID systems become increasingly fragile because of their lack of adaptability and the ability to understand local context to accurately detect the cyber-attacks in dynamic cyber environment like cloud computing. The current work investigates the design and deployment of context-awareness intrusion detection systems (IDS) based on advanced artificial intelligence (AI) approaches to improve a security framework in smart city. Utilizing the real time environmental, network and user behavior data, the IDS models introduced here can optimize detection parameters as dynamic, to improve the anomaly recognition, false positives’ reduction and response time. The research is aiming at combining context information from multiple and diverse internet of things devices, and sensors, and communication networks, so as to enable AI algorithms to build a complete situational awareness model. Such context-aware processing enables the IDS to draw the borderline between normal operation and attacks more accurately. Machine learning techniques, like deep learning and reinforcement learning, can be used to continuously enhance the power of detection over adaptive leaning and build robustness against zero-day attacks and adaptive threats. In addition, the software architecture highlights fog computing for real-time threat detection and transmission from edge to network and it emphasizes a decentralized processing system at the edge of the network while maintaining data privacy. Experimental results, analyzed on several simulated smart city networks, clearly show that the context-aware AI-based IDS performs considerably better than traditional IDSs on detecting a variety of cyber threats like DDoS attacks, data breaches, and unauthorized access attempts. The results demonstrate the system's strength, scalability, and efficiency, which points a promising trend for securing the digital backbone of the future smart cities. This work paves the way for smarter, secure urban ecosystems by leveraging AI and context-awareness in the domain of cybersecurity.

The rapid digitalization of European economies has fundamentally transformed traditional approaches to national economic security, creating a strategic need to integrate intelligent information systems (IIS) into state-level governance and financial architectures. Within the framework of European digital transformation and integration, the modernization of economic security mechanisms requires interoperability between national systems and European digital infrastructures. The study addresses the relevance of developing an architectural model for embedding IIS into the system of national economic security to enhance resilience, transparency, and evidence-based policymaking. The aim of the research is to determine how intelligent information systems can optimize decision-making and risk prevention within the economic security framework of states pursuing European digital integration. The object of study is the national economic security architecture influenced by artificial intelligence, big data, and automated monitoring technologies. The research applies a mixed-method approach combining system analysis, economic modeling, and comparative evaluation across EU and candidate states. The theoretical basis rests on the principles of digital governance, cyber resilience, and systems theory of economic security. Methodologically, the study employs multicriteria decision modeling (MCDM) and regression-based simulation using Eurostat and Digital Economy and Society Index (DESI) indicators to assess the effectiveness of IIS modules. Structural-functional and expert analysis methods are applied to identify causal links between intelligent monitoring systems and economic stability indicators, including fiscal transparency, financial control, and cyber resilience. Model simulations demonstrate that the implementation of IIS-driven monitoring mechanisms can reduce financial fraud, accelerate cyber incident response, and increase the efficiency of budget resource allocation. Consequently, the integration of intelligent systems enhances early threat detection, data interoperability, and coordination between national and European institutions responsible for economic stability. The results confirm that IIS integration contributes to a measurable rise in the national resilience index and strengthens compatibility with the European Economic Security Framework and Digital Europe Programme priorities. The practical value of the study lies in the proposed architectural model that unites analytical, infrastructural, and institutional layers of economic security governance. It provides a roadmap for harmonizing national security systems with the European Union’s standards of digital sovereignty and sustainable growth. Thus, the integration of intelligent information systems emerges as a crucial instrument for achieving long-term economic stability, innovation capacity, and digital independence within the evolving landscape of European digital transformation.

The rapid expansion of digital infrastructures and interconnected systems has intensified the demand for robust, adaptive, and intelligent network security mechanisms. Traditional security management approaches characterized by static rule sets, manual policy updates, and reactive incident responses are increasingly inadequate for addressing the complexity and dynamism of modern cyber threats. This study proposes a Model for Strengthening Network Security Through Intelligent Policy Automation and Compliance Systems, which integrates Artificial Intelligence (AI), Machine Learning (ML), and Software-Defined Networking (SDN) to create a proactive, self-adaptive security framework. The model automates policy enforcement, compliance verification, and threat mitigation through data-driven analytics and real-time decision-making. The proposed architecture employs a multi-layer design, consisting of a policy management layer for automated rule generation, an intelligence layer for anomaly detection and behavioral analysis, and a control layer that leverages SDN controllers for dynamic network configuration. By integrating ML algorithms with continuous monitoring systems, the framework enables context-aware security adaptation, ensuring that network defenses evolve in tandem with changing traffic patterns and threat landscapes. Furthermore, compliance with regulatory standards such as GDPR, ISO/IEC 27001, and NIST frameworks is maintained through automated auditing and policy validation mechanisms, reducing human error and ensuring consistent governance. Simulation and evaluation of the model demonstrate significant improvements in threat detection accuracy, response speed, and policy consistency compared to traditional rule-based systems. The framework’s intelligent automation minimizes manual intervention while enhancing transparency and accountability through explainable AI (XAI) mechanisms. This research contributes to the advancement of autonomous cybersecurity ecosystems, promoting resilience, scalability, and trust in digital enterprises. Future developments will explore federated learning, blockchain-based policy verification, and integration with quantum-safe security protocols to further strengthen adaptive, compliant, and intelligent network security management.