Europe is currently facing novel security threats in many different areas, reinforcing the need for a well prepared and protected health and care workforce to ensure health system resilience and service provision for the population under conditions of a poly-crisis. However, the health and care workforce is weakened by persisting shortages, competency gaps and mismatches, and poor working and mental health conditions. Health and care workers are not prepared for yet another crisis and a systematic strategy is lacking. This policy commentary argues for health and care workforce preparedness and protection as a structural pillar and integral part of an emerging EU health and security landscape, calling for a coherent European Union strategy and highlighting capacities for implementation and co-benefits for democratic societies and economies. Key policy recommendations include: developing a coordinated EU strategy that is capable to protect, prepare and retain health and care workers; closing the competencies gaps to align preparedness for military aggression, cyberattacks, climate change, and new infectious diseases; investing in research and data spaces to strengthen evidence-based information and policy; creating governance structures and building on existing EU programs and budgets to freeing resources for the health and care workforce.

LIMA: Towards building a non-invasive and stealthy real-world adversarial attack model for traffic sign recognition systems.

Timely resolution and disclosure of vulnerabilities are essential for maintaining the security of open-source software. However, many vulnerabilities remain unreported, unpatched, or undisclosed for extended periods, exposing users to prolonged security threats. While various vulnerability detection tools exist, they primarily focus on predicting or identifying known vulnerabilities, often failing to capture vulnerabilities that experience significant delays in resolution. In this study, we examine the vulnerability lifecycle by analyzing protracted vulnerabilities (PCVEs), which remain unresolved or undisclosed over long periods. We construct a dataset of PCVEs and conduct a qualitative analysis to uncover underlying causes of delay. To assess current automated solutions, we evaluate four state-of-the-art (SOTA) vulnerability detectors on our dataset. These tools detect only 1,059 out of 2,402 PCVEs, achieving approximately 44% coverage. To address this limitation, we propose DeeptraVul , an enhanced detection approach designed specifically for protracted cases. DeeptraVul integrates multiple development artifacts and code signals, supported by a Large Language Model (LLM)-based summarization component. For comparison, we also evaluate a standalone LLM. Our results show that DeeptraVul improves detection performance, achieving a 14% increase in coverage across all PCVEs and reaching 90% coverage on the DeeptraVul PCVE subset, outperforming existing SOTA detectors and standalone LLM based inference.

Abstract—Cloud computing has emerged as a pillar of the contemporary digital infrastructure, with the capacity to scale, low costs, and the ability to access data and services everywhere. Nevertheless, the sheer embrace of cloud platforms has come with a major challenge pertaining to the issue of data security, privacy and trust. The current paper outlines a more detailed method of how to improve data protection in the cloud computing setting, entailing the combination of sophisticated cryptographic algorithms, intelligent access control systems, and the use of AI and detector models. The framework proposed covers such key security issues as unauthorized access, data leakage, data breaches, and insider threats. The paper identifies encryption schemes such as symmetric and asymmetric cryptography, homomorphic encryption and secure multi-party computation, as the means to maintain the confidentiality of data stored and relayed. Also, the role-based access control (RBAC) and attribute-based access control (ABAC) models are included to implement fine-grained authorization policies. To build on this added protection, blockchain is proposed to audit data using secure and tamper-proof demonstrations, increasing transparency and accountability to the cloud systems. An important work in this regard is the incorporation of machine learning and artificial intelligence algorithms in real- time threat detection and tracking of anomalies. These models process high-traffic volumes of network traffic and user behavior patterns in order to proactively predict potential security threats and prevent attacks including the Distributed Denial of Service (DDoS), phishing and malware injections. The data integrity verification based on the use of hashing techniques and digital signatures is also a priority in the proposed system. The experimental findings indicate that the proposed scheme is significantly effective in enhancing the metrics of data security, such as low chances of breach, improved detection rates, and less time taken to respond to threats as compared to the traditional methods of security. Also, the framework will guarantee the adherence to data protection policies and assist in achieving cloud security on the multi-cloud environment data sharing. Finally, this study suggests that a multi-layered security approach that uses a combination of encryption, access control, blockchain, and AI-based monitoring are essential in protecting Identify applicable funding agency here. If none, delete this. sensitive data in cloud computing. The next-generation work will aim to enhance computational overhead optimization, scalability, and investigate quantum-resistant cryptographic methods to meet new security demands in the next-generation cloud infrastruc- ture. Index Terms—component, formatting, style, styling, insert

The rapid adoption of smart home Internet of Things (IoT) technologies has intensified the demand for secure, efficient, and privacy-preserving user authentication mechanisms. Existing revocable biometric-based authentication schemes provide certain security advantages; however, they often suffer from high computational complexity and limited adaptability to evolving security threats. This paper proposes a novel and efficient authentication framework for smart home IoT networks that integrates revocable biometrics with an optimized secret sharing protocol. A dynamic biometric revocation and update mechanism is introduced to ensure secure template replacement and long-term privacy preservation. Furthermore, a risk-based multi-factor authentication scheme is incorporated, enabling adaptive authentication levels based on real-time risk assessment. The proposed system employs a PUF-based mechanism to prevent device cloning attacks and ensures resistance against replay, man-in-the-middle, and stolen device attacks. Performance evaluations demonstrate improved computational efficiency, scalability, and enhanced privacy protection, making the framework suitable for resource-constrained smart home IoT environments.

Abstract— In the evolving digital landscape, cloud computing has become a cornerstone for scalable and flexible IT infrastructure. However, the rapid adoption of cloud services introduces a wide range of security threats, compliance challenges, and operational risks. This paper, From Threats to Trust: Securing Cloud Infrastructure through Resilience and Compliance presents a comprehensive analysis of cloud security challenges and explores effective mitigation strategies to enhance resilience and regulatory adherence. The study examines critical issues such as data breaches, insider threats, vendor lock-in, insecure interfaces, and distributed denial-of-service (DDoS) attacks within cloud environments. It further highlights the importance of robust security mechanisms, including identity and access management, encryption techniques, continuous monitoring, intrusion detection systems, and secure key management. Emphasis is also placed on compliance with global data protection regulations and the implementation of governance frameworks. By integrating best practices with proactive risk management approaches, this paper provides a structured framework for building secure, resilient, and compliant cloud infrastructures. The findings aim to support organizations in strengthening their security posture, ensuring data integrity and availability, and fostering trust in cloud-based systems. Keywords— Cloud Security, Cloud Computing, Identity and Access Management and Intrusion Detection Systems.

Emergency response squads in Israel represent a unique mix between civilian volunteerism and military-style security functions, operating in communities exposed to persistent security threats. This study examined how members of Emergency Response Squads negotiated their professional identity, role ambiguity, and physical readiness within a framework that decreases the clarity of traditional civil–military boundaries. Using a mixed-methods design, we surveyed 21 squad members with the Hebrew version of the International Physical Activity Questionnaire (IPAQ-S-H) and conducted semi-structured interviews to explore perceptions of organizational structure and readiness. Findings revealed a strong sense of community commitment but significant lacks in institutional support, standardized training, and physical fitness requirements. These shortages created tensions between the expectation of military-level performance and the voluntary nature of participation. We propose the Volunteer Readiness Integration Model, a conceptual framework linking community identity, organizational infrastructure, physical competence, and social resilience.

ABSTRACT This paper explores the discourse of two radical right populist parties, the British Reform UK and the French Rassemblement National, on immigration from the perspective of ontological security – the security of being, in terms of identity and autonomy. From a Lacanian perspective, ontological security is, however, unattainable, its illusion sustained by a fantasmatic narrative of a wholeness to come. In the discourse of both parties, this takes the form of the restoration of a monocultural national identity rooted in a shared history and a common culture and values. However, in the fantasmatic narrative, this ontological security is threatened by an obstacle, often an intruding Other who may be depicted as a Lacanian ‘Neighbour’, a violent figure who threatens to steal ‘pure’ citizens’ enjoyment of their Nation. In this context, immigrants are depicted, in both parties’ discourse, as evil Neighbour figures, who not only undermine national culture and values and enjoy benefits at the expense of the ‘pure’ members of the nation but who are strongly associated with criminality, including rape and murder. Thus, having posed immigration as a multidimensional security threat in this way, both parties propose draconian measures to reduce both regular and irregular migration.

Vehicular Adhoc Networks (VANET) play a critical role in intelligent transportation systems by enabling real-time communication among vehicles and infrastructure. However, due to their highly dynamic topology, decentralized architecture, and high node mobility, VANET environments are vulnerable to multiple security threats such as Distributed Denial of Service (DDoS), Sybil, and Blackhole attacks. Existing traditional systems primarily rely on individual machine learning models for attack detection, which often struggle to handle multivariate attack scenarios and fail to adapt effectively to rapidly changing network conditions. These approaches suffer from limitations such as high false alarm rates, poor generalization, and reduced detection accuracy under varying traffic patterns. To address these challenges, there is a strong need for a robust and adaptive detection framework that can accurately identify multiple attack types while maintaining consistency across dynamic environments. The proposed system introduces a hybrid stacking-based model and also baseline classifiers like Gaussian Naïve Bayes (GNB) and Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). The model leverages a Stacked Tree Alternating Optimization (TAO) Tree along with EDA-driven feature optimization to enhance detection performance. A meta-learning layer combines the outputs of base models to improve overall prediction stability and reduce misclassification. The system is further implemented as a Flaskbased web application to enable real-time attack detection, monitoring, and visualization through an interactive interface. The proposed approach demonstrates improved precision, reduced false alarms, and strong adaptability across diverse VANET traffic conditions. This work highlights the significance of hybrid stacking techniques in strengthening VANET security and provides a scalable solution for secure and efficient intelligent transportation systems

Kogartap I/Jakarta plays a strategic role in maintaining order in the capital city, executing state ceremonial duties, and supporting both military and non-military operations. In carrying out these tasks, the unit faces a number of potential risks, both internal—such as disruptions in communication and logistics systems—and external, including natural disasters, security threats, or social crises. As a result, the implementation of a Business Continuity Plan (BCP) is critically important as a risk mitigation strategy aimed at ensuring the organization's functionality during operational disruptions. This study seeks to analyze the extent to which BCP can be effectively applied within Kogartap I/Jakarta, and how it contributes to enhancing the organization’s readiness and responsiveness in crisis situations. The research employs a descriptive qualitative approach, using data collection techniques such as interviews, observation, and document analysis. It is expected that this study will strengthen BCP system recommendations to ensure operational continuity and the sustainability of routine garrison tasks in achieving the unit’s operational and strategic objectives.

The article substantiates demographic and labour atrophy of human potential erosion and an independent security threat to Ukraine’s national resilience under conditions of prolonged polycrisis and full-scale war. It is demonstrated that demographic losses, labour market distortions, behavioural and cognitive inertia of the population, and the deterioration of biomedical characteristics are not isolated socio-economic risks but rather form a cumulative atrophic contour that undermines the resilience of socio-economic systems and limits the development of the state. The essence of human potential erosion is revealed as a multidimensional process integrating demographic, labour, economic, behavioural-cognitive, biomedical, spatial, and institutional components of the weakening of national resilience. It has been proven that under conditions of prolonged uncertainty demographic and labour atrophy acquire self-sustaining properties, transforming from a derivative consequence of crisis shocks into a factor of their reproduction. An indicative model for assessing demographic and labour atrophy in the security dimension is proposed using the method of integral indicators, which involves calculation of component, partial, and generalised integral atrophy indices across seven key dimensions. A system of statistical indicators is formed, and their weighting coefficients are substantiated using the logical design method. A scale for interpreting the levels of demographic and labour atrophy (from low to critical), a monitoring framework, and differentiated managerial implications depending on the depth of atrophic processes are developed. It is demonstrated that ignoring demographic and labour atrophy within the system of strategic planning creates the risk of forming a ‘recovery without people’ scenario and leads to long-term constraints on national development. The results obtained may be used to strengthen the analytical support of national security policy, spatial recovery strategies, and human potential governance in Ukraine.

The arrival of recent cybersecurity standards has raised the bar for security assessments in organizations, but existing techniques require a high manual effort. Threat analysis and risk assessment are used to identify security threats for new or refactored systems. Still, there is a lack of definition-of-done, so identified threats have to be validated which slows down the analysis. Existing literature has focused on the overall effectiveness of threat analysis, but no previous work has investigated what material must the analysts use to effectively validate the identified security threats. We conduct a controlled experiment with practitioners to investigate whether having some analysis material (either the system's graphical model or LLM-generated advice) is better than none, and whether having both the system's graphical model and LLM-generated advice is better than having only one of them. We run a pilot of the experiment with 41 MSc students, a think-aloud study with three practitioners, and the experiment survey with 68 recruited practitioners. Our main findings suggest that, in terms of additional material needed for threat validation, less is more. We also find that participants perceived the graphical model as equally useful compared to LLMs and that, despite LLMs not always providing conclusive advice, practitioners still perceived it as somewhat useful. The experimental material and data analysis scripts is publicly available in a replication package.

The rapid growth of Internet of Things (IoT) devices is accompanied by increasingly sophisticated security threats, including DDoS attacks, brute-force authentication attempts, and large-scale packet flooding. Traditional statistical methods for anomaly detection exhibit low robustness to noise and fail to account for the dynamic nature of IoT traffic. This results in a higher rate of false positives and reduced accuracy in attack identification. This paper proposes a hybrid approach to IoT traffic anomaly detection consisting of three stages: preliminary filtering of suspicious packets using a modified Z-score adjusted for sample size; adaptive probabilistic attack risk assessment based on a Bayesian classifier with a weighting function that amplifies the impact of significant deviations; final classification using an ensemble of models (Random Forest, SVM, and LSTM), which ensures robustness to noise and enables the identification of nonlinear dependencies in the data. Experimental evaluation on the UNSW-NB15 dataset, which includes both normal traffic and diverse attack scenarios, demonstrated that the proposed method achieved Precision = 89.1 %, Recall = 90.3 %, and F1-score = 89.9 %. The best results were observed in the analysis of message interval anomalies (up to 92 % accuracy), confirming the effectiveness of temporal features. The method outperformed classical algorithms (Rosner Test, Holt- Winters) and achieved comparable accuracy to autoencoder while requiring significantly fewer computational resources. The hybrid architecture enables adaptation to diverse attack types and reduces false alarms through the combination of statistical filtering and ensemble classification. Its noise resilience and low computational complexity make the method suitable for deployment in resource-constrained IoT environments. Future research directions include the integration of federated learning for decentralized anomaly detection and the use of self-adaptive neural architectures for predicting complex attack scenarios.

The implementation of various electronic document management systems necessitates protective measures against information security threats, which can lead to operational failures, financial losses, disruption of plans, and damage to business reputation. In this regard, the objective of the study is to enhance the security level of information flows in corporate sector electronic document management systems against information blocking threats initiated by internal users. To achieve this objective, a system of interconnected mathematical models is proposed, forming the conceptual foundation of a digital twin for analyzing the current state of information flows in the electronic document management system. The developed approach enables quantitative assessment of the impact of users’ violations of electronic document processing regulations on business processes. Based on the modeling results, optimization problems have been formulated and solved to develop strategies for managing information flow movement under conditions of uncertainty. The obtained results establish an objective foundation for formulating specific recommendations to improve document management processes with respect to information security aspects.

Reconstructing shredded and ripped-up documents is an essential component of forensic investigations, intelligence gathering, and legal evidence restoration. Criminals frequently destroy evidence by tearing or shredding documents to conceal information related to fraud, financial crimes, identity theft, and confidential operations. Traditional reconstruction methods rely heavily on manual labor, expert judgment, and time-consuming physical assembly. These manual processes are limited in scalability and accuracy, especially when handling thousands of irregular fragments generated by cross-cut shredders or irregular tearing patterns. With advancements in artificial intelligence, deep learning has emerged as a promising solution for automating the reconstruction of shredded documents. This paper presents a deep learning–driven framework that integrates computer vision, convolutional neural networks (CNNs), feature extraction, edge detection, similarity learning, and transformer-based OCR to reconstruct shredded and ripped documents with high accuracy. The proposed methodology begins with preprocessing and segmentation of shredded fragments, followed by CNNbased feature extraction to capture edge patterns, texture consistency, and shape signatures. A Siamese network architecture is employed to evaluate the similarity between fragment pairs and determine potential adjacency relationships. The reconstruction module utilizes graph-based alignment algorithms that combine edge compatibility scores with spatial arrangement predictions to generate a candidate layout for the reassembled document. Once reconstruction is complete or partially complete, an OCR-based text extraction module retrieves textual content from the reassembled page to support forensic interpretation. Experimental results demonstrate the model’s capability to reconstruct mechanically shredded, hand-torn, and irregularly fragmented documents under varying degrees of damage. Performance metrics indicate significant improvements in accuracy, time efficiency, and completeness compared to traditional methods. This research provides a scalable, intelligent, and automated approach for forensic teams, reducing reliance on manual sorting and improving investigation efficiency. The deep learning pipeline has the potential to assist law enforcement agencies, digital forensics experts, and intelligence organizations in cases where recovering destroyed documents is critical for solving crimes or preventing security threats. Overall, the proposed framework advances the application of AI in forensic science and establishes a foundation for future enhancements using generative models and multimodal learning.

ABSTRACT Serious Organised Crime (SOC) offences include high-value fraud and other financial crimes, and cybercrime. Until 2015, UK law did not consider cybercrime a SOC. Rising levels of SOC now impact more people in the UK than any other national security threat. UK law enforcement adopted the 4Ps (Protect, Prevent, Pursue and Prepare) strategy from counter-terrorism to tackle cybercrime. This paper is based on a study examining how law enforcement handles online crime (cyber-dependent and cyber-enabled crime). The study interviewed 23 law enforcement officers and 20 service providers, support organisations and victims of online crime. Participants revealed that UK law enforcement considered cybercrime a SOC after the 2017 WannaCry attack. Team Cyber UK has flourished, but now Organised Criminal Groups (OCGs) are operating successful business models committing cybercrime and fraud. OCG’s involvement and increasing levels of online crime have seemingly overwhelmed UK policing’s resources despite the increased resources in Team Cyber.

ABSTRACT The August 2021 withdrawal of U.S. forces from Afghanistan and the Taliban’s subsequent takeover sent shockwaves across Central Asia. While the return of the Taliban has been widely regarded as a threat to regional security, Afghanistan’s neighbors have taken a pragmatic approach to the regime, pursuing a policy of selective engagement. The February 2022 Russian invasion of Ukraine, now in its fourth year, has further deteriorated the security environment in Central Asia. This ongoing conflict has weakened Russia’s traditional role as a provider of military stability and has facilitated the expansion of China’s security presence in the region. The overall aim of this study is to examine how changes in the regional security environment, specifically the Taliban’s takeover of Afghanistan and Russia’s invasion of Ukraine, have impacted China’s counter-terrorism strategy in Central Asia. The study is divided into three distinct sections. Following the introduction, Section 2 presents an analytic framework for assessing strategic and institutional adaptation of security actors in the realm of counter-terrorism. Section 3 focuses on the period between the 9/11 attacks and the U.S. withdrawal from Afghanistan, mapping out the emergence and evolution of counter-terrorism cooperation across multilateral, minilateral, and bilateral levels. Section 4 examines strategic and institutional adaptation measures developed by China and its Central Asian partners in response to the Taliban’s 2021 takeover of Afghanistan. The findings of this study will contribute to the ongoing discussion on non-Western responses to transnational security threats, and how those responses relate to liberal interventionism.

ABSTRACT Amidst growing tensions between China and allies of the US (of which Australia is one), there is an increasingly prevalent view of China as a national security threat. At the same time, the US and its ally countries have also been favourite destinations for outbound immigrants from China. Against this geopolitical backdrop, this paper gauges the level of trust in mainstream news stories among individuals in the cohort of first-generation Chinese-Australian migrants from the PRC. It also aims to identify the key factors that lie behind an observed difference in the level of trust between members of this Chinese-Australian cohort and Australia’s general public, in relation to mainstream news stories about China or Chinese-Australians. Integrating quantitative survey data with insights from in-depth interviews, this paper points to the need to update existing understandings of the relationship between trust and the media in an era of changing geopolitical dynamics.

Abstract—Wearable health devices continuously collect and transmit personal medical data, making them highly susceptible to security threats. This paper proposes a hybrid cryptographic framework that combines Elliptic Curve Diffie-Hellman (ECDH) for secure key exchange, AES-GCM for efficient encryption, HMAC for data integrity, and ECDSA for authentication. The proposed model ensures confidentiality, integrity, and authenticity (CIA) of data with minimal computational overhead, making it suitable for resource-constrained environments. Experimental eval- uation on an ARM Cortex-M4 microcontroller shows improved speed and reduced energy consumption compared to traditional cryptographic methods. Index Terms—Wearable devices, Hybrid cryptography, AES- GCM, ECDH, ECDSA, Healthcare IoT, Data security.

The COVID-19 pandemic has redefined the outlines of national and global security, situating health emergencies as a crux within the domain of non-traditional security threats. For India, the pandemic examined the persistence of its healthcare framework and revealed the complicated linkages between human well-being and national governance under the ambit of human security. Unlike traditional security threats that emanate from military or political strife, pandemics appear as intangible, cross-border challenges that undermine societies from within. The outbreak drastically affected India’s socioeconomic fabric. It also put down peculiar trauma on public health institutions and accentuated drawbacks in health infrastructure, policy coordination, and crisis management. Pandemic as a non-traditional threat, featured that security can no longer be confined to territorial defence but must encompass the protection of human lives, livelihoods, and dignity. The concept of human security, which prioritizes human well-being, becomes vital in understanding India’s vulnerabilities and preparedness. The crisis underlines the need for an integrated health emergency policy within the broader framework of national security. Strengthening public health governance, fostering inter-governmental cooperation, and investing in research and technology are essential to building a resilient security architecture. Furthermore, India’s pandemic experience highlights the significance of global cooperation, as no nation can tackle such crises in isolation. Thus, pandemics challenge India to rethink its conventional security paradigm and need to adopt a comprehensive approach where health is recognized as a vital pillar of security. Addressing pandemics as non-traditional security challenges requires not only institutional reforms but also a shift in policy mindset from reactive containment to proactive preparedness rooted in the principles of human security and sustainable governance.