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Most existing adaptive information security approaches focus on simplified behavioral patterns and work as isolated models. This limits their effectiveness against advanced and dynamic cyber threats. Therefore, there is an emergent requirement for a mathematically unified framework that can dynamically capture and forecast the aggregate behavior of both the attacker and the defender in a complex environment. The paper proposes a mathematical modeling approach that combines composite behavior models into adaptive information security strategies. The framework encapsulates heterogeneous behavioral patterns into a unified dynamic model that can adapt to an ever-changing threat landscape. This result in novel adaptation rules derived from system dynamics and game theory, with the aim of enabling proactive defense mechanisms that can adapt to real-time challenges posed by adversary actors. The outcomes presented in this paper demonstrate strong improvements in threat detection, mitigation speed, and resource optimization through systematic model implementation, comprehensive simulation, and positive statistical hypothesis testing. The comparison reveals that the proposed method is generally superior to existing methods in scalability and effectiveness. It presents a new class of adaptive cybersecurity models that have deeper behavioral insights and enhanced resilience in complicated threat environments.

The rapid evolution of smart cities has led to transformative advancements through the integration of IoT devices, sensors, and data-driven systems, yet has simultaneously exposed critical vulnerabilities in cybersecurity, data integrity, and trust management. This research proposes a Decentralized Trust Framework that leverages blockchain technology, AI-driven threat detection, and a Lightweight Adaptive Proof-of-Stake (LA-PoS) consensus mechanism to address these challenges. The framework integrates three key layers: a Blockchain Layer for decentralized trust and immutability, a Cybersecurity Layer employing cryptographic standards and AI-based anomaly detection, and a Data Integrity Protocol Layer for real-time synchronization and tamper-proof data validation. Performance evaluations indicate the framework achieves a threefold increase in transaction throughput, a 30% reduction in latency, and enhanced energy efficiency compared to traditional blockchain systems. Security metrics highlight a 98.2% threat detection rate and a substantial reduction in false positives, while resource optimization nearly doubles IoT device battery life. The framework demonstrates applicability in critical smart city use cases, including smart traffic management, energy systems, and public safety, providing secure, scalable, and efficient solutions for urban infrastructures. Despite these advancements, challenges such as interoperability among heterogeneous systems, computational overhead for IoT devices, and policy adoption persist. Future research will focus on optimizing interoperability protocols, incorporating quantum-resistant cryptographic techniques, and extending the framework to emerging domains such as autonomous systems and smart healthcare. The proposed framework provides a robust foundation for building sustainable, resilient, and trustworthy urban ecosystems, bridging gaps in current smart city technologies.

PurposeThis paper aims to introduce the concept of a framework of cyber-security controls that are adaptable to different types of organisations and different types of employees. One of these adaptive controls, namely, the mode of training provided, is then empirically tested for its effectiveness.Design/methodology/approachIn total, 1,048 working Australian adults completed the human aspects of the information security questionnaire (HAIS-Q) to determine their individual information security awareness (ISA). This included questions relating to the various modes of cyber-security training they had received and how often it was provided. Also, a set of questions called the cyber-security learning-styles inventory was used to identify their preferred learning styles for training.FindingsThe extent to which the training that an individual received matched their learning preferences was positively associated with their information security awareness (ISA) level. However, the frequency of such training did not directly predict ISA levels.Research limitations/implicationsFurther research should examine the influence of matching cyber-security learning styles to training packages more directly by conducting a controlled trial where the training packages provided differ only in the mode of learning. Further research should also investigate how individual tailoring of aspects of an adaptive control framework (ACF), other than training, may improve ISA.Practical implicationsIf cyber-security training is adapted to the preferred learning styles of individuals, their level of ISA will improve, and therefore, their non-malicious behaviour, whilst using a digital device to do their work, will be safer.Originality/valueA review of the literature confirmed that ACFs for cyber-security does exist, but only in terms of hardware and software controls. There is no evidence of any literature on frameworks that include controls that are adaptable to human factors within the context of information security. In addition, this is the first study to show that ISA is improved when cyber-security training is provided in line with an individual’s preferred learning style. Similar improvement was not evident when the training frequency was increased suggesting real-world improvements in ISA may be possible without increasing training budgets but by simply matching individuals to their desired mode of training.

Internet of Things environments are comprised of heterogeneous devices that are continuously exchanging information and being accessed ubiquitously through lossy networks. This drives the need of a flexible, lightweight and adaptive access control mechanism to cope with the pervasive nature of such global ecosystem, ensuring, at the same time, reliable communications between trusted devices. To fill this gap, this paper proposes a flexible trust-aware access control system for IoT (TACIoT), which provides an end-to-end and reliable security mechanism for IoT devices, based on a lightweight authorization mechanism and a novel trust modelthat has been specially devised for IoT environments. TACIoT extends traditional access control systems by taking into account trust values which are based on reputation, quality of service, security considerations and devices’ social relationships. TACIoT has been implemented and evaluated successfully in a real testbed for constrained and non-constrained IoT devices.

Blockchain technology has revolutionized numerous industries by providing decentralized, transparent, and immutable ledgers. However, its adoption is hindered by persistent security challenges, including arbitrage attacks, liquidity exploits, and noncompliance with antimoney laundering (AML) regulations. This paper proposes an enhanced framework to address these issues, combining dynamic pricing mechanisms, AI‐based anomaly detection, and regulatory compliance checks within a multilayered architecture. The framework is composed of five interconnected layers: the input layer for data collection and validation, the data warehouse layer for structured data classification, the processing layer for anomaly detection and pricing adjustments, and the decision layer for transaction validation, execution, and reporting. The integration of these layers ensures robust security and compliance mechanisms, reducing system vulnerabilities while optimizing efficiency. To validate the proposed framework, we conducted simulations using real‐world blockchain scenarios, including decentralized finance (DeFi) platforms and cryptocurrency exchanges. Results demonstrate significant reductions in arbitrage opportunities and liquidity risks, with improved accuracy in anomaly detection and compliance adherence. For instance, the dynamic pricing mechanism mitigated 87% of arbitrage attack attempts, while the AI‐based anomaly detection achieved an 89% accuracy rate in identifying high‐risk transactions. This study provides actionable insights and a scalable solution for enhancing blockchain security and trust. Future work will focus on integrating cross‐chain interoperability, real‐time threat intelligence, and privacy‐preserving techniques to further expand the framework’s applicability. By addressing critical vulnerabilities, this research contributes to the development of secure, transparent, and compliant blockchain ecosystems, paving the way for wider adoption across industries. Unlike previous blockchain security models, our framework introduces a real‐time, AI‐enhanced risk assessment mechanism that dynamically updates transaction risk scores, mitigating financial threats in decentralized environments. This holistic approach provides a scalable, explainable, and adaptive security system that not only protects decentralized financial infrastructures but also aligns with emerging regulatory requirements, ensuring long‐term applicability.

This study provides a method for analyzing the drought-vulnerability index (DVI) from a multidimensional perspective that includes biophysical and social aspects, considering the Intergovernmental Panel on Climate Change’s (IPCC) assessment. The proposed method generates the “exposure index (EI)”, “sensitivity index (SI)”, and “adaptive capacity index (ACI)” components of the proposed DVI using nine sub-indicators and 29 proxy variables. By using it throughout all of Turkey’s provinces, the performance of the developed index was evaluated. In this study, the decision matrices were built utilizing expert knowledge, and the weights of the indicators and variables were obtained by using the Analytical Hierarchy Process (AHP) technique. Moreover, the values of these four indices were classified as “very high, high, moderate, low, and very low,” and their geographical distribution across the country was drawn, as well as relevant patterns retrieved. The study’s major results show that 17 of the 81 provinces are classified as “very high,” 16 as “high,” 15 as “moderate,” 17 as “low,” and the remaining 16 as “very low” drought vulnerable. Another significant result is that the majority of people in the country’s south, center, and southeast rely on agriculture and are thus more vulnerable to drought due to socioeconomic underdevelopment in those regions.

Image steganography is crucial for secure communication, enabling covert data embedding within cover images. While traditional methods such as LSB embedding are vulnerable to detection, deep learning techniques like GANs and autoencoders have improved performance, yet they still struggle with dynamic adaptation to diverse secret data types, limited training datasets, and resilience to distortions. To address these issues, we propose a flexible framework with adaptive multi-encoder-decoder pairs, extensive dataset training, and an optimized architecture with specialized components. Our model achieves significant improvements in Secret Recovery Accuracy (SRA), Stego-Image Quality (SSIM, PSNR), and robustness to noise, with SSIM of 0.99 and recovery accuracy over 98%. It also reduces the detection rate, with an AUC approaching 0.5 in steganalysis. These results set a new benchmark for secure image transmission and privacy-preserving communication.

Organic framework membranes (OFMs) have emerged as transformative materials for separation technologies due to their tunable porosity, structural diversity, and stability, yet their design and optimization face challenges in navigating vast chemical spaces and complex performance trade-offs. This review highlights the pivotal role of machine learning (ML) in overcoming these limitations by integrating multi-source data, constructing quantitative structure–property relationships, and enabling the cross-scale optimization of OFMs. Methodologically, ML workflows—spanning data construction, feature engineering, and model optimization—accelerate candidate screening, inverse design, and mechanistic interpretation, as demonstrated in gas separations and nascent liquid-phase applications. Key findings reveal that ML identifies critical structural descriptors and environmental parameters, guiding the development of high-performance membranes that surpass traditional selectivity–permeability limits. Challenges persist in liquid separations due to dynamic operational complexities and data scarcity, while emerging frameworks offer untapped potential. The integration of interpretable ML, in situ characterization, and industrial scalability strategies is essential to transition OFMs from laboratory innovations to sustainable, adaptive separation systems. This review underscores ML’s transformative capacity to bridge computational insights with experimental validation, fostering next-generation membranes for carbon neutrality, water security, and energy-efficient industrial processes.

Phishing detection models degrade quickly due to drift, adversarial evasion, and fairness issues. Existing MLOps platforms mainly automate deployment and monitoring. Prior works have examined SHAP-based monitoring, retraining, or fairness audits separately, but lack an integrated theory of resilience for adversarial environments. We introduce the Hybrid MLOps Framework (HAMF), a system designed to embed resilience and ethical governance into the lifecycle of phishing detection models. HAMF is ‘hybrid’ because it unifies proactive and reactive adaptation, combining automation with stakeholder oversight, and embedding resilience with ethical governance. HAMF treats resilience as an integrated lifecycle property, designed to simultaneously preserve model accuracy, fairness, and stakeholder trust amidst concept drift. Methodologically, HAMF implements this through a hybrid control cycle. This cycle fuses four key capabilities: SHAP-guided feature replacement, event-driven retraining, fairness-triggered audits, and structured human feedback. Unlike conventional pipelines where these functions are isolated, HAMF ensures their interdependence as first-class triggers. Empirical evaluations on large-scale phishing streams demonstrate HAMF’s superior performance. The framework detects drift within 18 seconds, restores F1 scores above 0.99 post-attack, reduces subgroup disparities by over 60%, and scales to over 2,300 requests per second with sub-50ms latency. These results validate HAMF’s design, demonstrating that embedding resilience and ethical alignment into the MLOps lifecycle is both effective and scalable.

This study addresses the growing complexity of privacy protection in cloud computing environments (CCEs) by introducing a comprehensive socio-technical framework for self-adaptive privacy, complemented by an AI-driven beta tool designed for social media platforms. The framework’s three-stage structure—social, technical, and infrastructural—integrates context-aware privacy controls, dynamic risk assessments, and scalable implementation strategies. Key benefits include enhanced user-centric privacy management through customizable group settings and adaptive controls that respect diverse social identities. The beta tool operationalizes these features via a profile store for structured preference management and a recommendation engine that delivers real-time, AI-powered privacy suggestions tailored to individual contexts. Additionally, the tool’s safety scoring system (0–100) empowers developers and guides them in designing effective privacy solutions and mitigating risks. By bridging social context awareness with technical and infrastructural innovation, this framework significantly improves privacy adaptability, regulatory compliance, and user empowerment in CCEs. It provides a robust foundation for developing scalable and responsive privacy solutions tailored to evolving user needs.