Geopolitical fractures in energy markets: Cross-dimensional analysis of security, socioeconomic, and policy landscapes under Russia-Ukraine conflict

Recent advances in crosstalk simulation using integer-order memristive synapses have shown considerable progress. However, most existing models still employ a single-memristor structure, which constrains synaptic weight modulation and makes it difficult to represent both excitatory and inhibitory synaptic connections in a unified manner. These models also often fail to capture the memory effects and nonlocal dynamic properties inherent in biological neurons. To address these issues, this study introduces a fractional-order memristive bridge synapse model for crosstalk coupling. By combining Hindmarsh–Rose (HR) and FitzHugh–Nagumo (FN) neurons, we construct an 8D heterogeneous coupled neural network based on fractional calculus—designated as the Fractional-Order Memristive Bridge Crosstalk-Coupled Neural Network (FMBCCNN). A major innovation is the incorporation of a fractional-order memristive bridge structure that mimics synaptic connections in a bridge configuration. This design provides both historical memory characteristics and bidirectional synaptic weight regulation, overcoming limitations of traditional coupling forms.<br>Using dynamical analysis tools such as phase portraits, bifurcation diagrams, and Lyapunov exponents, we systematically investigate how synaptic and crosstalk strengths influence system behavior under conventional fractional-order conditions. The results reveal diverse dynamical behaviors, including attractor coexistence, forward and reverse period-doubling bifurcations, and chaotic crises. Further analysis under the more generalized condition of non-uniform fractional orders shows that, compared with the conventional case, the system maintains continuous periodic motion over broader parameter ranges and exhibits clear parameter hysteresis. Although local dynamic patterns remain similar, the corresponding parameter intervals are substantially widened. In addition, the system displays more concentrated and marked alternation between periodic and chaotic behaviors. We also simulate the effect of varying the fractional-order derivative, offering a more general mathematical characterization of neuronal firing activity.<br>Finally, the chaotic sequences generated by the system are applied to an image encryption algorithm incorporating bit-plane decomposition and DNA encoding. Security analysis confirms that the encrypted images have pixel correlation coefficients below 0.01 in horizontal, vertical, and diagonal directions, information entropy greater than 7.999, and a key space of 2<sup>2080</sup>. These results verify the excellent encryption performance and reliability of the proposed scheme and the generated sequences.

Why do states adapt to threats posed by non-state armed groups armed Groups’ actors despite their conventional military superiority? This study examines the review of Israel’s military doctrine between 2008 and 2023, with a focus on the decisive component as a central element, despite the state’s overwhelming conventional and technological superiority. The research examines the impact of asymmetric threats, particularly those posed by Hamas, on the development of Israel’s ability to make rapid and decisive decisions and achieve clear outcomes in conflicts. The study relies on a comprehensive analysis of official Israeli military and security documents to identify the mechanisms linking asymmetric threats to the review of the decisiveness component within the military doctrine. The findings indicate a shift from reliance on deterrence and preemptive strikes toward concepts of active defense and conflict management between wars, accompanied by an enhanced capacity for decisiveness in military operations. This transformation reflects a dynamic strategic adaptation to a changing and asymmetric security environment, rather than a weakness in Israel’s military or technological capabilities.

Security and Confidentiality of patient information are important in the modern healthcare system. Patient information is often stored on digital platforms through digital health records, telemedicine, and remote monitoring. The proposed work presents a cryptographic authentication framework for healthcare monitoring that uses AES-256 and Virtual Password Authentication(VPF) to protect sensitive data. The Virtual Password Function (VPF) is a little trick that combines a secret function with a code booking technique. This technique prevents unauthorized users from compromising security. It mitigates password-based attacks. Patient data is stored in a completely encrypted way to meet healthcare privacy mandates. The proposed system was developed in Java for encryption and matching authentication of processes. The implementation uses AES-256 encryption to safeguard patient data. It includes custom authentication logic for managing virtual passwords. The cloud uses encrypted end-to-end patient information and stores it in MySQL. The scalable and maintainable front-end web interface and backend control logic are developed using Java JSP Servlet. The framework provides secure, adequate protection of sensitive healthcare data in digital health ecosystems by leveraging strong encryption and adaptive authentication. As shown by experimental results and security analysis, the proposed model is effective for healthcare applications requiring high-level security. It offers relatively low execution, processing, key generation, and encryption/decryption times, alongside enhanced security.

Federated Learning (FL) offers a privacy-preserving distributed learning paradigm by enabling model training without direct access to raw data. However, FL remains vulnerable to unauthorized access during training and client-server exchanges. Authentication and key agreement are essential to restrict access to legitimate participants. Existing FL authentication schemes are prone to impersonation risks, centralized PKI fragility, and insufficient integrity guarantees. To address these challenges, we propose DA[Formula: see text]4FL, a robust dynamic accumulator-based authentication and key agreement with preserving data integrity for FL. Specifically, our proposed DA[Formula: see text]4FL is an efficient authentication protocol utilizing dynamic accumulators, blockchain technology, and message authentication codes, which ensures robust member management, authorized access, and data integrity. Security analysis against the eCK adversary model confirms the resilience of our protocol. Furthermore, experiments and performance evaluations show the effectiveness of our method, with computational overhead competitive with current state-of-the-art (SOTA) baselines.

The subject of the research is the development and implementation of an eye health monitoring system using modern technologies, in particular wireless sensor networks, biometric sensors and software for automatic detection of vision diseases. Special attention is paid to methods of processing and analyzing data from sensors for accurate diagnosis of pathologies such as cataracts, glaucoma, diabetic retinopathy and other eye diseases. The aim of the work is to create a system that allows detecting visual impairments in real time, performing automatic diagnostics and providing treatment recommendations. The system integrates with a mobile application and can work together with other medical devices to facilitate patient-doctor interaction. The tasks solved in the article: 1) develop a system for collecting and monitoring eye health data; 2) create algorithms for processing and analyzing the obtained data; 3) develop a mobile application; 4) test the developed system. Methods used in the study: data analysis from biometric sensors, algorithms for automatic comparison of indicators with a database of normal and pathological values, and wireless data transmission technologies (Bluetooth, Wi-Fi). The developed database and software provide secure storage and analysis of medical data. Results. The results of the study showed that the system allows monitoring the state of vision in real time with high accuracy (85–90%), detecting pathologies in the early stages and automatically notifying the patient and doctor about detected deviations. The system demonstrates effectiveness in early detection of diseases and allows for timely prescribing of treatment or additional examinations. Conclusions. The developed system is an important step towards integrating medical technologies into everyday life. It provides timely detection of vision disorders and convenient access to monitoring results. In the future, it is possible to expand the functions to detect other eye diseases and integrate with additional medical devices for comprehensive monitoring of the patient's health.

Proof of sequential work (PoSW), as an emerging cryptographic primitive, is designed to provide a verifiable method for proving that a computational process has incurred a real and continuous expenditure of time. This characteristic demonstrates its significant application potential in decentralized systems, time-stamping services, and trusted computing. This paper systematically reviews and discusses the developmental trajectory, typical variants, potential attacks, and diverse applications of PoSW. Concurrently, it places a special emphasis on analyzing the evolutionary path and application scenarios of its important special case—the verifiable delay function (VDF) aiming to provide a comprehensive reference for research and practice in related fields.

Although cookies introduced as session authentication tokens in Hypertext Transfer Protocol (HTTP) resolve its stateless limitation, their static nature introduces vulnerabilities to cross-site scripting (XSS) attacks. Attackers exploit unfiltered user input to inject malicious scripts into web applications, enabling theft of user cookies for session hijacking. While HTTP Secure (HTTPS) employs Transport Layer Security (TLS) to encrypt communications, it remains susceptible to client-side script injection vulnerabilities that bypass TLS protections. Current cookie session hijacking protections focus on credential security but remain vulnerable to link-layer attacks. To address this challenge, we propose a novel Cookie Authentication Scheme against XSS Attacks (CAXSS) for HTTPS. The CAXSS scheme uses signatures to the messages exchanged by the original HTTPS protocol to achieve mutual identity authentication. Specifically, clients authenticate cookies using digital signatures based on Elliptic Curve Cryptography (ECC), while servers reject unsigned cookies. This approach ensures that only legitimate clients can generate valid cookie credentials, thwarting unauthorized cookie reuse. The results of security analysis and performance evaluations demonstrate that the CAXSS scheme is secure and effective.

Dynamic Searchable Encryption (DSE) is essential for enabling confidential search operations over encrypted data in cloud computing. However, all existing single-server DSE schemes are vulnerable to Keyword Pair Result Pattern (KPRP) leakage and fail to simultaneously achieve forward and backward security. To address these challenges, this paper proposes a conjunctive keyword DSE scheme based on a dual-server architecture (DS-CKDSE). By integrating a full binary tree with an Indistinguishable Bloom Filter (IBF), the proposed scheme adopts a secure index: The leaf nodes store the keywords and the associated file identifier, while the information of non-leaf nodes is encoded within the IBF. A random state update mechanism, a dual-state array for each keyword and the timestamp trapdoor designs jointly enable robust forward and backward security while supporting efficient conjunctive queries. The dual-server architecture mitigates KPRP leakage by separating secure index storage from trapdoor verification. The security analysis shows that the new scheme satisfies adaptive security under a defined leakage function. Finally, the performance of the proposed scheme is evaluated through experiments, and the results demonstrate that the new scheme enjoys high efficiency in both update and search operations.

The Health Data Space (HDS) is a promising platform for the secure health data sharing among entities including patients and healthcare providers. However, health data is highly sensitive and critical for diagnosis, and unauthorized access or destruction by malicious users can lead to serious privacy leaks or medical negligence. Thus, robust access control, privacy preservation, and data integrity are essential for HDS. Although Ciphertext-Policy Attribute-Based Encryption (CP-ABE) supports secure sharing, it has limitations when directly applied to HDS. Many current schemes cannot simultaneously handle data integrity violations, trace and revoke malicious users, and protect against privacy leaks from plaintext access policies, with key escrow being another major risk. To overcome these issues, we put forward a Traceable and Revocable Privacy-Preserving Data Sharing (TRPPDS) scheme. Our solution uses a novel distributed CP-ABE with a large universe alongside data auditing to provide fine-grained, key-escrow-resistant access control over unbounded attributes and guarantee data integrity. It also features tracing-then-revocation and full policy hiding to thwart malicious users and protect policy privacy. Formal security analysis is presented for our proposal, with thorough performance assessment also demonstrates its feasibility in HDS.

Visible Light Communication (VLC) systems commonly employ optical orthogonal frequency division multiplexing (O-OFDM) to achieve high data rates, benefiting from its robustness against multipath effects and intersymbol interference (ISI). However, a key limitation of asymmetrically clipped direct current biased optical–OFDM (ACO-OFDM) systems lies in their inherently high peak-to-average power ratio (PAPR), which significantly affects signal quality and system performance. This paper proposes a joint chaotic encryption and modified μ-non-linear logarithmic companding (μ-MLCT) scheme for ACO-OFDM–based VLC systems to simultaneously enhance security and reduce PAPR. First, image data is encrypted at the upper layer using a hybrid chaotic system (HCS) combined with Arnold’s cat map (ACM), mapped to quadrature amplitude modulation (QAM) symbols and further encrypted through chaos-based symbol scrambling to strengthen security. A μ-MLCT transformation is then applied to mitigate PAPR and enhance both peak signal-to-noise ratio (PSNR) and bit-error-ratio (BER) performance. A mathematical model of the proposed secured ACO-OFDM system is developed, and the corresponding BER expression is derived and validated through simulation. Simulation results and security analyses confirm the effectiveness of the proposed solution, showing gains of approximately 13 dB improvement in PSNR, 2 dB in BER performance, and a PAPR reduction of about 9.2 dB. The secured μ-MLCT-ACO-OFDM not only enhances transmission security but also effectively reduces PAPR without degrading PSNR and BER. As a result, it offers a robust and efficient solution for secure image transmission with low PAPR, making it well-suitable for emerging wireless networks such as cognitive and 5G/6G systems.

Unmanned aerial vehicle (UAV) networks have become an essential component of modern civilian and military infrastructures. However, the communication channels between UAVs and their control entities remain vulnerable to spoofing and message tampering attacks. Although conventional digital signature schemes can ensure message authentication and integrity, they often undermine the real-time responsiveness of UAV operations and fail to protect the privacy of signers. To address these limitations, we propose an expressive attribute-based proxy signature (EABPS) scheme tailored for UAV networks. The scheme enables fine-grained authorization and authentication, ensuring that only entities whose attributes satisfy a specified access structure can generate valid proxy signatures. Furthermore, the scheme preserves signer privacy by decoupling signatures from explicit identities. Comprehensive security analysis and extensive experimental evaluation demonstrate that the proposed EABPS scheme achieves strong security guarantees while offering improved computational efficiency and expressiveness, making it a practical solution for secure communication in UAV networks.

The integration of operational technology (OT) and information technology (IT) within the Industrial Internet of Things (IIoT) has posed prominent security challenges for resource-constrained devices. Existing authentication architectures often suffer from critical vulnerabilities: one is their reliance on centralized trusted third parties, which creates single points of failure; the other is their use of static credentials like biometrics, which pose severe privacy risks if compromised. To address these limitations, this paper proposes DLR-Auth, which combines chaotic synchronization of semiconductor superlattice physically unclonable functions (SSL-PUFs) with Shamir’s secret sharing (SSS) to enable decentralized registration and revocable templates. Notably, DLR-Auth is a two-party authentication framework that removes the need for a separate online registration authority that operates directly between a user device (UDi) and a server (S). In our setting, the server S still acts as the central relying party and hardware authority embedding the matched SSL-PUF module. The protocol also includes an efficient multi-access mechanism optimized for high-frequency interactions. Formal security analysis with the Real-or-Random (ROR) model proves the semantic security of the session key, while performance evaluations demonstrate that DLR-Auth has significant advantages in computational and communication efficiency. DLR-Auth thus offers a robust, scalable, lightweight solution for next-generation secure IIoT systems.

In the context of privacy-preserving face recognition systems, entropy plays a crucial role in determining the efficiency and security of computational processes. However, existing schemes often encounter challenges such as inefficiency and high entropy in their computational models. To address these issues, we propose a privacy-preserving face recognition method based on the Face Feature Coding Method (FFCM) and symmetric homomorphic encryption, which reduces computational entropy while enhancing system efficiency and ensuring facial privacy protection. Specifically, to accelerate the matching speed during the authentication phase, we construct an N-ary feature tree using a neural network-based FFCM, significantly improving ciphertext search efficiency. Additionally, during authentication, the server computes the cosine similarity of the matched facial features in ciphertext form using lightweight symmetric homomorphic encryption, minimizing entropy in the computation process and reducing overall system complexity. Security analysis indicates that critical template information remains secure and resilient against both passive and active attacks. Experimental results demonstrate that the facial authentication efficiency with FFCM classification is 4% to 6% higher than recent state-of-the-art solutions. This method provides an efficient, secure, and entropy-aware approach for privacy-preserving face recognition, offering substantial improvements in large-scale applications.

This study aims to analyze legal protection for transaction security in the metaverse and the challenges faced by consumers in the virtual world. The method used is a normative juridical approach with a qualitative approach, combining the Statute Approach to examine laws and regulations, the Conceptual Approach to examine legal concepts related to digital assets, smart contracts, and cybersecurity, and the Comparative Approach to compare consumer protection practices and international regulations in other countries. The research focuses on analyzing the authority of cyber law in protecting users, evaluating the effectiveness of existing regulations, identifying legal challenges resulting from the unique characteristics of the metaverse, and developing legal policy recommendations. The results show that national regulations provide basic protection, but are still limited in addressing the unique risks of virtual transactions, such as digital fraud, hacking, and smart contract disputes. Key legal challenges include cross-border jurisdiction, the legal status of digital assets, and the security of immersive data. This study recommends regulatory updates, transaction security standards, digital dispute resolution mechanisms, and cross-jurisdictional collaboration to strengthen consumer protection. The implications of this research provide a basis for developing adaptive cyber law policies in Indonesia and serve as a reference for international practices in regulating transactions in the metaverse.

The proliferation of binary vulnerabilities in the software supply chain has become a critical security challenge. Existing vulnerability detection approaches—including dynamic analysis, static analysis, and decompilation-assisted analysis—all suffer from limitations such as insufficient coverage, high false-positive and false-negative rates, or poor compatibility. Although decompilation technology can serve as a bridge connecting binary-code and source-code vulnerability detection tools, current schemes suffer from inadequate semantic restoration quality and lack of tool compatibility. To address these issues, this paper proposes LLMVulDecompiler, a binary decompilation model based on fine-tuned large language models designed to generate high-precision decompiled code that integrates directly with source-code static analysis tools. We construct a dedicated training and evaluation dataset that covers multiple compiler optimization levels (e.g., O0–O3) and a diverse set of program functionalities. We adopt a two-stage fine-tuning strategy that involves first building foundational decompilation capabilities, then enhancing vulnerability-specific features. Additionally, we design a low-cost inference pipeline and establish multi-dimensional evaluation criteria, including restoration similarity, compilation success rate, and functional correctness. Experimental results show that the model significantly outperforms baseline models in terms of average edit distance, compilation success rate, and black-box test pass rate on the HumanEval-C benchmark. In tests on 12 real-world CVE (Common Vulnerabilities and Exposures) instances, the approach achieved a detection accuracy of 91.7%, with substantially reduced false-positive and false-negative rates. This study demonstrates the effectiveness of specialized fine-tuning of large language models for binary decompilation and vulnerability detection, offering a new pathway for binary security analysis.

Traditional network security analysis methods exhibit critical limitations in processing high-dimensional dynamic data, including inefficient feature selection, poor adaptability to evolving threats, and low detection sensitivity below 50%. To address these challenges, this study proposes a multi-objective multi-label feature selection model integrated with an optimized Fireworks Algorithm. The Improved Fireworks Algorithm Model incorporates Gaussian operators and adaptive functions while fusing fuzzy neural networks to enhance real-time threat response. Experimental validation across Palmer Penguin (small-scale), Fashion MNIST (medium-scale), and Bike Sharing (large-scale) datasets demonstrates three key advancements: Data processing capacity reaches 5,000 samples, exceeding Particle Swarm Optimization and standard Fireworks Algorithm baselines by 66%; Sensitivity maintains 70%-100% across datasets, outperforming traditional methods by 30% points; In a medium-sized data set, the research method scored only 5 out of 10 in the five indicators of comprehensive performance comparison based on the weighted geometric mean of the five-dimensional radar chart, indicating that the research method may have problems of overfitting or insufficient generalization ability when processing complex data. Adaptive adjustment time is reduced by 50%, confirming significant efficiency gains. These findings establish a robust framework for dynamic network security while highlighting scalability constraints in complex data environments.

Federated Learning and Artificial Intelligence (AI) are two most intriguing and leading technologies in the intelligent healthcare business. Data must be collected, stored and analyzed from various companies. Patient data processing, particularly in the medical industry, promises substantial breakthroughs in customized health care. For example, different hospitals preserve electronic health records (EHR) for different patient populations, which are difficult to communicate between hospitals due to their concern. As electronic health record (EHR) systems have developed more widespread, privacy and security concerns have increased significance. Sharing and analyzing sensitive health data across multiple organizations and stakeholders requires the application of sophisticated privacy-protection strategies. This study offerings a Secure Federated Transfer Learning (SFTL) structure with an improved system to address privacy and security issues in smart EHR systems. While preserving the privacy of individual patient records, the SFTL framework enables various healthcare providers to interact and collaboratively train machine learning (ML) models using their dispersed EHR data. SMPC enhances privacy guarantees by securely calculating statistical metrics and model parameters across multiple participants without disclosing their contributions. Based on a real-world smart EHR dataset, we evaluate the proposed SFTL -SMPC architecture and compare its performance to existing privacy-preserving techniques. The results demonstrate that our strategy, SFTL-SMPC, balances data privacy and model precision, outperforming traditional federated learning approaches while maintaining patient data security. In addition, we conduct a comprehensive security analysis to validate that our SFTL-SMPC implementation is resilient against various attack scenarios.

The exponential growth of Internet infrastructure and the widespread adoption of smart sensing devices have empowered industrial personnel to conduct remote, real-time data analysis within the Industrial Internet of Things (IIoT) framework. However, transmitting this real-time data over public channels raises significant security and privacy concerns. To prevent unauthorized access, user authentication mechanisms are crucial in the IIoT environment. To mitigate security vulnerabilities within IIoT environments, a novel user authentication and key agreement protocol is proposed. The protocol is designed to restrict service access exclusively to authorized users of designated smart sensing devices. By incorporating cryptographic hash functions, chaotic maps, Physical Unclonable Functions (PUFs), and fuzzy extractors, the protocol enhances security and functional integrity. PUFs provide robust protection against tampering and cloning, while fuzzy extractors facilitate secure biometric verification through the integration of smart cards, passwords, and personal biometrics. Moreover, the protocol accommodates dynamic device enrollment, password and biometric updates, and smart card revocation. A rigorous formal security analysis employing the Real-or-Random (ROR) model was conducted to validate session key security. Complementary informal security analysis was performed to assess resistance to a broad spectrum of attacks. Comparative performance evaluations unequivocally demonstrate the protocol’s superior efficiency and security in comparison to existing benchmarks.

In current medical data sharing practices, the tension between data privacy protection and cross-institutional collaboration efficiency has become increasingly prominent. To address existing security challenges in healthcare data sharing, we propose a collaborative data cooperation model based on blockchain and federated learning, Through federated learning technology, data is made “usable but not visible” by enabling medical institutions to share only encrypted model parameters, thereby preventing the leakage of raw data. Meanwhile, blockchain technology is introduced to establish a decentralized trust mechanism, utilizing smart contracts to automate data access management and track training processes. In addition, the dual security protection strategy is designed, where differential privacy and Paillier homomorphic encryption technology are adopted to resist member reasoning attacks and ensure secure storage and sharing of information. Through security analysis and experimental validation, the scheme has been proven to have good security and usability.