Search Results (822)

Digital signal processing plays a central role in intelligent railway communications under high-mobility, strong-multipath, and time-varying-channel conditions. This review surveys representative techniques for multi-carrier modulation, precoding, index modulation, and chaos-inspired physical layer security and highlights their mathematical foundations. Core themes include transform-domain representations typified by time–frequency analysis, linear-algebraic formulations of precoding and equalization, combinatorial structures underlying index mapping and spectral efficiency gains, and nonlinear dynamical systems theory of chaotic encryption. The methods are compared in terms of bit error performance, peak-to-average power ratio, spectral efficiency, computational complexity, and information security, with emphasis on railway-specific deployment constraints. The synergistic application of these methods with intelligent railway transportation systems is expected to enhance the overall performance of railway transportation systems in terms of transmission efficiency, reliability, and security. It provides critical technological support for the efficient and secure operation of next-generation intelligent transportation systems. Full article

Unmanned Aerial Vehicles (UAVs) are increasingly integrated into Internet of Things (IoT) ecosystems for applications such as surveillance, disaster response, environmental monitoring, and logistics. These missions demand reliable and secure communication between UAVs and cloud platforms for command, control, and data storage. However, UAV communication channels are highly vulnerable to eavesdropping, spoofing, and man-in-the-middle attacks due to their wireless and often long-range nature. Traditional cryptographic schemes either impose excessive computational overhead on resource-constrained UAVs or lack sufficient robustness for cloud-level security. To address this challenge, we propose a dual-layer encryption architecture that balances lightweight efficiency with strong cryptographic guarantees. Unlike prior dual-layer approaches, the proposed framework introduces a context-aware adaptive lightweight layer for UAV-to-gateway communication and a hybrid post-quantum layer for gateway-to-cloud security, enabling dynamic cipher selection, energy-aware key scheduling, and quantum-resilient key establishment. In the first layer, UAV-to-gateway communication employs a lightweight symmetric encryption scheme optimized for low latency and minimal energy consumption. In the second layer, gateway-to-cloud communication uses post-quantum asymmetric encryption to ensure resilience against emerging quantum threats. The architecture is further reinforced with optional multi-path hardening and blockchain-assisted key lifecycle management to enhance scalability and tamper-proof auditability. Experimental evaluation using a UAV testbed and cloud integration shows that the proposed framework achieves 99.85% confidentiality preservation, reduces computational overhead on UAVs by 42%, and improves end-to-end latency by 35% compared to conventional single-layer encryption schemes. These results confirm that the proposed adaptive and hybridized dual-layer design provides a scalable, secure, and resource-aware solution for UAV-to-cloud communication, offering both present-day practicality and future-proof cryptographic resilience. Full article

The exponential growth of digital data and the escalating sophistication of cyber threats have intensified the demand for secure yet computationally efficient encryption methods. Conventional algorithms (e.g., AES-based schemes) are cryptographically strong and widely deployed; however, some implementations can face performance bottlenecks in large-scale or real-time workloads. While many modern systems seed from hardware entropy sources and employ standardized cryptographic PRNGs/DRBGs, security can still be degraded in practice by weak entropy initialization, misconfiguration, or the use of non-cryptographic deterministic generators in certain environments. To address these gaps, this study introduces FileCipher. This novel file-encryption framework integrates a chaos-enhanced Cryptographically Secure Pseudorandom Number Generator (CPRNG) based on the State-Based Tent Map (SBTM). The proposed design achieves a balanced trade-off between security and efficiency through dynamic key generation, adaptive block reshaping, and structured confusion–diffusion processes. The SBTM-driven CPRNG introduces adaptive seeding and multi-key feedback, ensuring high entropy and sensitivity to initial conditions. A multi-threaded Java implementation demonstrates approximately 60% reduction in encryption time compared with AES-CBC, validating FileCipher’s scalability in parallel execution environments. Statistical evaluations using NIST SP 800-22, SP 800-90B, Dieharder, and TestU01 confirm superior randomness with over 99% pass rates, while Avalanche Effect analysis indicates bit-change ratios near 50%, proving strong diffusion characteristics. The results highlight FileCipher’s novelty in combining nonlinear chaotic dynamics with lightweight parallel architecture, offering a robust, platform-independent solution for secure data storage and transmission. Ultimately, this paper contributes a reproducible, entropy-stable, and high-performance cryptographic mechanism that redefines the efficiency–security balance in modern encryption systems. Full article

The incorporation of artificial intelligence (AI) and digital technology in healthcare has revolutionized service delivery, improving diagnostic precision, patient outcomes, and operational efficacy. Nonetheless, despite considerable progress, numerous problems persist that impede the realization of full potential. Current reviews predominantly emphasize the advantages of AI in disease detection and health guidance, neglecting significant concerns such as social opposition, regulatory frameworks, and geographical discrepancies. This SLR, executed in accordance with PRISMA principles, examined 21 publications from 2020 to 2025 to assess the present condition of AI and digital technologies inside Saudi Arabia’s healthcare industry. Initially, 863 publications were obtained, from which 21 were chosen for comprehensive examination. Significant discoveries encompass the extensive utilization of telemedicine, data analytics, mobile health applications, Internet of Things, electronic health records, blockchain technology, online platforms, cloud computing, and encryption methods. These technologies augment diagnostic precision, boost patient outcomes, optimize administrative procedures, and foster preventative medicine, contributing to cost-effectiveness, environmental sustainability, and enduring service provision. Nonetheless, issues include data privacy concerns, elevated implementation expenses, opposition to change, interoperability challenge, and regulatory issues persist as substantial barriers. Subsequent investigations must concentrate on the development of culturally relevant AI algorithms, the enhancement of Arabic natural language processing, and the establishment of AI-driven mental health systems. By confronting these challenges and utilizing emerging technologies, Saudi Arabia has the potential to establish its status as a leading nation in medical services innovation, guaranteeing patient-centered, efficient, and accessible healthcare delivery. Recommendations must include augmenting data privacy and security, minimizing implementation expenses, surmounting resistance to change, enhancing interoperability, fortifying regulatory frameworks, addressing regional inequities, and investing in nascent technologies. Full article

The rapid digitization of library resources has intensified the need for robust digital-rights management (DRM) mechanisms to safeguard copyright, control access, and preserve user privacy. Conventional DRM approaches are often centralized, prone to single-point-of-failure, and are limited in transparency and interoperability. To address these challenges, this article puts forward a decentralized DRM framework for library systems by leveraging blockchain technology and decentralized DRM-key mechanisms. An integrative review of the available research literature provides an analysis of current blockchain-based DRM library systems, their limitations, and associated challenges. To address these issues, a controlled experiment is set up to implement and evaluate a possible solution. In the proposed model, digital content is encrypted and stored in the Inter-Planetary File System (IPFS), while blockchain smart contracts manage the generation, distribution, and validation of DRM-keys that regulate user-access rights. This approach ensures immutability, transparency, and fine-grained access control without reliance on centralized authorities. Security is enhanced through cryptographic techniques for authentication. The model not only mitigates issues of piracy, unauthorized redistribution, and vendor lock-in, but also provides a scalable and interoperable solution for modern digital libraries. The findings demonstrate how blockchain-enabled DRM-keys can enhance trust, accountability, and efficiency through the development of secure, decentralized, and user-centric digital library systems, which will be of interest to practitioners charged with library IT technology management and to researchers in the wider field of blockchain applications in organizations. Full article

Unmanned Aerial Systems (UASs) are playing an increasingly critical role in both civilian and defense applications. However, their heavy reliance on unencrypted Global Navigation Satellite System (GNSS) signals, particularly GPS, makes them highly susceptible to signal spoofing attacks, posing severe operational and safety threats. This paper introduces a comprehensive, AI-driven multi-layer sensor framework that simultaneously enables real-time spoofing detection and secure command-and-control (C2) communication in lightweight UAS platforms. The proposed system enhances telemetry reliability through a refined preprocessing pipeline that includes a novel GPS Drift Index (GDI), robust statistical normalization, cluster-constrained oversampling, Kalman-based noise reduction, and quaternion filtering. These sensing layers improve anomaly separability under adversarial signal manipulation. On this enhanced feature space, a differentiable architecture search (DARTS) approach dynamically generates lightweight neural network architectures optimized for fast, onboard spoofing detection. For secure command and control, the framework integrates a low-latency cryptographic layer utilizing PRESENT-128 encryption and CMAC authentication, achieving confidentiality and integrity with only 1.79 ms latency and a 0.51 mJ energy cost. Extensive experimental evaluations demonstrate the framework’s outstanding detection accuracy (99.99%), near-perfect F1-score (0.999), and AUC (0.9999), validating its suitability for deployment in real-world, resource-constrained UAS environments. This research advances the field of AI-enabled sensor systems by offering a robust, scalable, and secure navigation framework for countering GPS spoofing in autonomous aerial vehicles. Full article

Cloud storage continues to experience recurring provider-side breaches, raising concerns about the confidentiality and recoverability of user data. This study addresses this challenge by introducing an Artificial Intelligence (AI)-driven hybrid architecture for secure, reconstruction-resistant multi-cloud storage. The system applies telemetry-guided fragmentation, where fragment sizes are dynamically predicted from real-time bandwidth, latency, memory availability and disk I/O, eliminating the predictability of fixed-size fragmentation. All payloads are compressed, encrypted with AES-128 and dispersed across independent cloud providers, while two encrypted fragments are retained within a VeraCrypt-protected local vault to enforce a distributed trust threshold that prevents cloud-only reconstruction. Synthetic telemetry was first used to evaluate model feasibility and scalability, followed by hybrid telemetry integrating real Microsoft system traces and Cisco network metrics to validate generalization under realistic variability. Across all evaluations, XGBoost and Random Forest achieved the highest predictive accuracy, while Neural Network and Linear Regression models provided moderate performance. Security validation confirmed that partial-access and cloud-only attack scenarios cannot yield reconstruction without the local vault fragments and the encryption key. These findings demonstrate that telemetry-driven adaptive fragmentation enhances predictive reliability and establishes a resilient, zero-trust framework for secure multi-cloud storage. Full article

Encryption-then-compression (EtC) based on block scrambling enables privacy-preserving image sharing while maintaining compatibility with standard image codecs, yet it disrupts the spatial correlations and synchronization cues required by conventional reversible data hiding (RDH). This difficulty is further amplified in grayscale-based EtC pipelines, where RGB-to-YCbCr conversion and component serialization introduce representation shifts and non-bijective rounding/clamping effects, complicating reliable embedding and extraction. This paper presents a unified RDH framework compatible with both RGB-based and grayscale-based block-scrambling EtC systems, without altering the underlying encryption procedures. The core idea is to restore embedding and extraction synchronization directly in the encrypted domain using two encryption-invariant cues: diagonal pixel absolute difference (DPAD) and an encryption-invariant synchronization index (EISI), together with domain-consistent handling of the grayscale conversion pipeline. Experimental results on standard datasets demonstrate perfect reversibility and stable embedding performance under the evaluated settings, with negligible impact on lossless compressibility. We further observe that the proposed embedding can increase statistical dispersion within encrypted blocks; although not designed as a security enhancement, this effect degrades the performance of representative texture-based analyses in the considered ciphertext-only setting. Full article

The current intrusion detection methods suffer from deficiencies in terms of cross-domain adaptability, privacy preservation, and limited effectiveness in detecting minority-class attacks. To address these issues, a novel intrusion detection model framework, TrMulS, is proposed that integrates federated learning, generative adversarial networks with multispace feature enhancement ability, and transformers with multi-source transfer ability. First, at each institution (source domain), local spatial features are extracted through a CNN, multiple subsets are constructed (to solve the feature singularity problem), and the multihead self-attention mechanism of the transformer is utilized to capture the correlation of features. Second, the synthetic samples of the target domain are generated on the basis of the improved Exchange-GAN, and the cross-domain transfer module is designed by combining the Maximum Mean Discrepancy (MMD) to minimize the feature distribution difference between the source domain and the target domain. Finally, the federated transfer learning strategy is adopted. The model parameters of each local institution are encrypted and uploaded to the target server and then aggregated to generate the global model. These steps iterate until convergence, yielding the globally optimal model. Experiments on the ISCX2012, KDD99 and NSL-KDD intrusion detection standard datasets show that the detection accuracy of this method is significantly improved in cross-domain scenarios. This paper presents a novel paradigm for cross-domain security intelligence analysis that considers efficiency, privacy and balance. Full article

The quality and safety of grain and oil food are paramount to sustainable societal development and public health. Implementing early warning analysis and risk control is critical for the comprehensive identification and management of grain and oil food safety risks. However, traditional risk prediction models are limited by their inability to accurately analyze complex nonlinear data, while their reliance on centralized storage further undermines prediction credibility and traceability. This study proposes a deep learning risk prediction model integrated with a blockchain-based traceability mechanism. Firstly, a risk prediction model combining Grey Relational Analysis (GRA) and Bayesian-optimized Tabular Neural Network (TabNet-BO) is proposed, enabling precise and rapid fine-grained risk prediction of the data; Secondly, a risk prediction method combining blockchain and deep learning is proposed. This method first completes the prediction interaction with the deep learning model through a smart contract and then records the exceeding data and prediction results on the blockchain to ensure the authenticity and traceability of the data. At the same time, a storage optimization method is employed, where only the exceeding data is uploaded to the blockchain, while the non-exceeding data is encrypted and stored in the local database. Compared with existing models, the proposed model not only effectively enhances the prediction capability for grain and oil food quality and safety but also improves the transparency and credibility of data management. Full article

This paper proposes a process-aware, tag-based digital identity framework that enhances interoperability while enabling identity unlinkability and selective disclosure across multi-party workflows involving sensitive data. We realize this framework within the self-sovereign identity (SSI) paradigm, employing zk-SNARK–based zero-knowledge proofs to enable verifiable identity authentication without plaintext disclosure. The framework introduces a protocol-tagging mechanism to support multiple proof systems within a unified architecture, thereby enhancing SSI scalability and interoperability. Its core innovation lies in combining identity unlinkability and process-driven data disclosure: derived sub-identities mitigate identity-linkage attacks, while layered encryption enables selective, stepwise decryption of sensitive information (e.g., delivery addresses), ensuring participants access only the minimal information necessary for their tasks. In addition, zero-knowledge proof-based verification guarantees that the validation of derived sub-identities can be performed without sharing any plaintext attributes or identifying factors. We applied the framework to logistics, where sub-identities anonymize participants and layered encryption allows for delivery addresses to be decrypted progressively along the logistics chain, with only the final courier authorized to access complete information. During the parcel receipt process, users can complete verification using derived sub-identities and zero-knowledge proofs alone, without disclosing any real personal information or attributes that could be linked back to their identity. Trusted Execution Environments (TEEs) ensure the authenticity of decryption requests, while blockchain provides immutable audit trails. A demonstration system was implemented, formally verified using Scyther, and performance-tested across multiple platforms, including resource-constrained environments, showing high efficiency and strong practical potential. The core paradigms of identity unlinkability and process-driven data disclosure are generalizable and applicable to multi-party scenarios involving sensitive data flows. Full article

With the rapid development of online aquatic product trading, traditional centralized platforms are facing increasing pressure in terms of data security, privacy protection, and trust. Problems such as tampering with transaction records, weak identity authentication, privacy leakage, and the difficulty of balancing matching efficiency with security limit the further development of these platforms. To address these issues, this paper proposes a blockchain-based identity authentication and access control scheme for online aquatic product trading. The scheme first introduces a dual authentication mechanism that combines a verifiable random function with a Schnorr-based zero-knowledge proof, providing strong decentralized identity verification and resistance to replay attacks. It then designs a dynamic access control strategy based on a multi-dimensional reputation model, which converts user behavior, attributes, and historical transaction performance into a comprehensive trust score used to determine fine-grained access rights. In addition, an AES-PEKS hybrid encryption method is employed to support encrypted keyword search and order matching while protecting the confidentiality of order data. This paper implements a multi-channel architecture for aquatic product trading prototype system on Hyperledger Fabric. This system separates registration, order processing, and reputation management into different channels to improve concurrency and enhance privacy protection. Security analysis shows that the proposed solution effectively defends against replay attacks, key leaks, data tampering, and privacy theft. Performance evaluation further demonstrates that, compared to a single-chain architecture, the multi-channel design, while increasing security mechanisms, maintains a stable throughput of approximately 223 tx/s even when concurrency reaches 600–800 tx/s, ensuring normal operation of the trading system. These results indicate that this solution provides a practical technical approach and system-level reference for building secure, reliable, and efficient online aquatic product trading platforms. Full article

The efficiency optimization of encryption and decryption algorithms in cloud environments is addressed in this study, where the processing speed of encryption and decryption is enhanced through the application of multi-threaded parallel technology. In view of the high-concurrency and distributed storage characteristics of cloud platforms, a multi-threaded concurrency mechanism is adopted for the direct processing of data streams. Compared with the traditional serial processing mode, four distinct encryption algorithms, namely AES, DES, SM4 and Ascon, are employed, and different data units are processed concurrently by means of multithreaded technology. Based on multi-dimensional performance evaluation indicators (including throughput, memory footprint and security level), comparative analyses are carried out to optimize the design scheme; accordingly, multi-threaded collaborative encryption is realized to improve the overall operation efficiency. Experimental results indicate that, in comparison with the traditional serial encryption method, the encryption and decryption latency of the algorithm is reduced by around 50%, which significantly lowers the time overhead associated with encryption and decryption processes. Simultaneously, the throughput of AES and DES algorithms is observed to be doubled, which leads to a remarkable improvement in communication efficiency. Moreover, under the premise that the original secure communication capability is guaranteed, system resource overhead is effectively reduced by SM4 and Ascon algorithms. On this basis, a quantitative reference basis is provided for cloud platforms to develop targeted encryption strategies tailored to diverse business demands. In conclusion, the proposed approach is of profound significance for advancing the synergistic optimization of security and performance in cloud-native data communication scenarios. Full article

Cross-site scripting (XSS) attacks are among the threats facing web security, resulting from the diversity and complexity of HTML formats. Research has shown that some text processing-based methods are limited in their ability to detect this type of attack. This article proposes an approach aimed at improving the detection of this type of attack, taking into account the limitations of certain techniques. It combines the effectiveness of deep learning represented by convolutional neural networks (CNN) and the accuracy of classification methods represented by support vector machines (SVM). It takes advantage of the ability of CNNs to effectively detect complex visual patterns in the face of injection variations and the SVM’s powerful classification capability, as XSS attacks often use obfuscation or encryption techniques that are difficult to be detected with textual methods alone. This work relies on a dataset that focuses specifically on XSS attacks, which is available on Kaggle and contains 13,686 sentences in script form, including benign and malicious cases associated with these attacks. Benign data represents 6313 cases, while malicious data represents 7373 cases. The model was trained on 80% of this data, while the remaining 20% was allocated for test. Computer vision techniques were used to analyze the visual patterns in the images and extract distinctive features, moving from a textual representation to a visual one where each character is converted into its ASCII encoding, then into grayscale pixels. In order to visually distinguish the characteristics of normal and malicious code strings and the differences in their visual representation, a CNN model was used in the analysis. The convolution and subsampling (pooling) layers extract significant patterns at different levels of abstraction, while the final output is converted into a feature vector that can be exploited by a classification algorithm such as an Optimized SVM. The experimental results showed excellent performance for the model, with an accuracy of (99.7%), and this model is capable of generalizing effectively without the risk of overfitting or loss of performance. This significantly enhances the security of web applications by providing robust protection against complex XSS threats. Full article

Hierarchical asynchronous federated learning (HAFL) accommodates more real networking and ensures practical communications and efficient aggregations. However, existing HAFL schemes still face challenges in balancing privacy-preserving and robustness. Malicious training nodes may infer the privacy of other training nodes or poison the global model, thereby damaging the system’s robustness. To address these issues, we propose a secure hierarchical asynchronous federated learning (SHAFL) framework. SHAFL organizes training nodes into multiple groups based on their respective gateways. Within each group, the training nodes prevent inference attacks from the gateways and committee nodes via a mask–DP exchange protocol and employ homomorphic encryption (HE) to prevent collusion attacks from other training nodes. Compared with conventional solutions, SHAFL uses noise that can be eliminated to reduce the impact of noise on the global model’s performance, while employing a shuffle model and subsampling to enhance the local model’s privacy-preserving level. At global model aggregation, SHAFL considers both model accuracy and communication delay, effectively reducing the impact of malicious and stale models on system performance. Theoretical analysis and experimental evaluations demonstrate that SHAFL outperforms state-of-the-art solutions in terms of convergence, security, robustness, and privacy-preserving capabilities. Full article