Search Results (356)

The increasing interdependence between environmental degradation, geopolitical instability, and public-health pressures requires structured information-system architectures capable of integrating heterogeneous data and transforming them into decision-support knowledge. In this context, this study develops a data-driven information system architecture for the exploratory analysis of environmental, geopolitical, and health-related risks in the EU-27 during 2013–2023. The proposed system is structured as a multi-layered analytical pipeline designed to process country-year panel data and generate interpretable outputs. The methodological framework integrates Principal Component Analysis (PCA) for exploratory dimensionality reduction, K-Means clustering for structural pattern identification, a RandomTree classification model for translating cluster membership into decision rules, and a Two-Part Fixed Effects Model. Experimental results indicate an optimal and interpretable clustering configuration at k = 3, revealing three broad structural profiles among EU Member States. A moderate positive relationship is identified between greenhouse gas emissions per capita (GHGE) and health expenditure (SHA) (r = 0.34), while geopolitical risk (GPR) exhibits weak and statistically insignificant associations. This association is interpreted cautiously, as it may reflect the combined effect of industrial activity, environmental exposure, economic development, and the higher financial capacity of some Member States to allocate resources to healthcare systems. The results indicate the dominant contribution of GHGE and SHA in differentiating the identified profiles, while GPR shows limited explanatory power within the analyzed context. The RandomTree model achieved an accuracy of 93.58% in reproducing the cluster labels; however, it is used as an interpretability layer rather than as an independent validation of clustering. The system supports the identification of vulnerability-related structural patterns and provides an exploratory basis for future data-driven monitoring and early-warning applications. Full article

To address the limitations of existing chaotic systems such as complex structure and potential chaotic degradation, this paper proposes a novel four-dimensional discrete chaotic system (4D-DCS) and an image encryption algorithm based on it. The 4D-DCS is constructed by integrating a feedback controller and modulo operation into a linear discrete-time system, featuring a simple structure without the need for intricate matrix reconstruction or memristor circuits. Mathematical analysis confirms its chaos in the sense of Li–Yorke and numerical simulations including Lyapunov exponent (LE) analysis, 0–1 test, and NIST SP 800-22 test demonstrate its hyperchaotic characteristics and excellent pseudorandomness. Based on the 4D-DCS, the proposed encryption algorithm employs SHA-256 to generate initial states for key uniqueness, combines row–column permutation to disrupt pixel correlation, and adopts a reversible neural network for diffusion to enhance confusion capability. Comprehensive security analysis shows that the algorithm achieves an NPCR of ∼99.61% and a UACI of ∼33.46%, a key space of $2^{216}$, information entropy close to 8, and correlation coefficients of encrypted images near 0. It also exhibits strong robustness against differential, cropping, noise, and chosen-plaintext attacks. Comparative analysis with state-of-the-art algorithms validates the 4D-DCS’s advantages in structural simplicity and stability, and the encryption algorithm’s superiority in security and practicality, making it suitable for security-critical applications such as image encryption. Full article

We propose a secure biometric data protection (SBDP) system, which uses artificial intelligence (AI) and encryption methods to prevent forgery and keep the biometric data private and intact. The proposed SBDP approach integrates deep learning-based feature extraction with robust encryption and authentication mechanisms in a single pipeline. We use the optimized convolutional neural network (OCNN) to obtain unique features from multimodal biometric inputs like fingerprints, facial photos, and retinal scans. This works well because it learns how to represent data efficiently. To reduce the risks of raw biometric exposure, we adopt a generative adversarial network (GAN) to generate synthetic biometric representations that maintain essential characteristics while reducing sensitivity to data leakage. The biometric features and images are encrypted using the ElGamal cryptosystem to provide security assurance, while the digital signature scheme based on the SHA-256 hash function is used to provide data integrity and authenticity. Experimental results show good performance of all components of the framework. The optimized CNN obtains a classification accuracy of more than 99.8%, while the GAN shows stable training behavior with the discriminator and generator losses converging to around 0.3 and 4.0, respectively. The cryptographic module guarantees encryption dependability and signature verification efficacy across all evaluated scenarios. The integrated system provides effective protection of biometric data from unauthorized access, tampering and identity forgery. The SBDP framework is a promising solution for defense, healthcare and digital identity management, ensuring secure transmission and storage of biometric data. Full article

Tool-calling LLM agents are vulnerable to indirect prompt injection: externally retrieved data can redirect tool calls without system-prompt access, and prompt-level defences leave three harm classes undefended (path traversal, user-guided exfiltration, high-frequency tool abuse). We present a Policy Enforcement Point (PEP) that intercepts at the tool-call boundary with declarative rules over a cross-step information-flow label system (source integrity, data sensitivity) and a synchronous SHA-256 hash-chained audit log. On a controlled dataset across four attack classes, the full system cuts the attack success rate (ASR) from 40.0% to 5.0% (deepseek-v4-pro, five repeats) versus 35.0% for the strongest prompt-only baseline; disabling cross-step label propagation raises the call-level false-negative rate by 26.4 points. The 30.0% task-level false-positive rate is dominated by by-design least-privilege capability-token denials, not rule false positives—an expanded 30-task benign set yields 0/30 rule false positives under scripted isolation. A conservative-DS mitigation (intent-taint) closes the constructed denied-read reconstruction blind-spot variant (ASR 100% to 0%) at no cost on standard workflows. The audit log detects all three tested tamper classes; the in-process enforcement overhead is sub-millisecond per call. Across four further backends, ASR drops under the full system, though LLaMA-3.3-70B retains 16.7% (a rule-coverage gap). A preliminary run over a real MCP stdio transport (an official filesystem server) shows the mechanism operates at a real boundary with a sub-millisecond execution-path increment. We frame these as mechanism-coverage evidence on a controlled benchmark, not a deployability claim for production MCP workloads. Code, data, and metrics are openly available in the replication repository. Full article

The Fifth Industrial Revolution (Industry 5.0) requires human-centric mechanisms that preserve the integrity, reproducibility, and interpretability of AI-driven decisions in smart manufacturing. Injection molding generates heterogeneous, imbalanced, and weakly labeled process data, posing reliability and integrity risks to data-driven quality control. This study proposes an integrity-verified and reproducibility-instrumented secure machine learning framework for operating-regime analysis in injection molding that integrates (i) SHA-256-based data-integrity verification at ingestion, (ii) Pearson correlation-based feature selection, and (iii) a Gaussian Mixture Model (GMM) under a passive-adversary threat model with Transport Layer Security (TLS)-secured transmission. Evaluated on real industrial data (n = 6719 cycles, seven process variables), correlation-based feature selection retained four non-redundant variables and improved the GMM Silhouette Score from 0.274 ± 0.075 (all features) to 0.323 ± 0.014 (95% CI [0.318, 0.329]), a +18.2% relative improvement (paired t(29) = 3.39, p = 0.002; Cohen’s d = 0.62; Wilcoxon p = 0.022), while lowering the Davies–Bouldin Index from 1.63 to 1.17. The Silhouette standard deviation of 0.014 over 30 seeds meets the σ ≤ 0.02 reproducibility target. The GMM resolves four interpretable operating regimes—one low-load regime consistent with nominal operation and three elevated-load regimes (left-side, right-side, and bilateral)—with operator-readable per-variable signatures. Relative to hard-partition and projection baselines, the GMM is not Silhouette-optimal but provides an interpretable, generative regime model that meets the σ ≤ 0.02 reproducibility target. The framework operationalizes human-centric manufacturing security as measurable integrity, reproducibility, and interpretability. Full article

In resource-constrained UAV swarm systems, multimodal sensory data are often affected by complex environmental factors, resulting in modality missing, signal degradation, and asynchrony, which significantly reduce the reliability of multimodal learning and incremental model updates. To address this issue, we propose a Compensatory Collaboration Modality-Balanced Sample Selection framework (CC-MBS), which improves robustness through modality quality modeling and cross-UAV collaborative compensation. Specifically, a modality confidence vector is introduced to quantify modality reliability from missing rate, degradation, and asynchrony. A lightweight collaboration mechanism is designed to exchange low-dimensional confidence information instead of high-dimensional features or model parameters. Based on the compensated confidence, a modality-aware sample selection strategy is further developed to prioritize high-value samples under limited memory. Experimental results in simulated UAV-swarm-inspired benchmark settings show that CC-MBS outperforms representation-based methods such as ShaSpec and its parameter aggregation variants (AVG, PFM, POW) in both modality compensation accuracy and communication–computation efficiency under missing conditions. In addition, it achieves stronger robustness than MBS and training-dynamics-based methods such as EL2N and GraNd in sample selection. These results demonstrate that CC-MBS effectively improves robustness and data efficiency for multimodal incremental learning under incomplete modalities. Full article

With the prevalence of telemedicine and digital diagnosis, the security and integrity of medical images transmitted over open networks have become critical issues. To effectively defend against malicious tampering and ensure the reliability of diagnostic information, this study proposes a block-based image authentication and tamper detection framework (KPP-BA). This framework integrates key-dependent pixel permutation, hash-based message authentication code (HMAC)-SHA256 hash verification, and a parity-based 3-LSB minimal distortion embedding strategy. The core innovation lies in utilizing pseudo-random pixel permutation to disrupt spatial correlation within blocks, thereby effectively resisting collage and statistical analysis attacks. Furthermore, by combining the avalanche effect of HMAC-SHA256 with hybrid bit-plane feature extraction, the proposed method ensures extremely high sensitivity to subtle tampering. Experimental results on a dataset comprising 300 medical images demonstrate that the proposed method maintains superior visual quality while ensuring security, achieving an average Peak Signal-to-Noise Ratio (PSNR) of 54.15 of 0.5 bit per pixel (bpp). Moreover, against various tampering attacks—including masking, copy–paste, circle masking, and collage—the method exhibits exceptional detection capabilities with an average detection accuracy of 99.99%. Compared with seven state-of-the-art methods, the proposed framework demonstrates significant advantages in both image fidelity and tamper localization precision, validating its feasibility and robustness for secure medical image transmission applications. Full article

SYN flooding attacks remain a persistent threat to network availability, particularly in Distributed Denial-of-Service (DDoS) scenarios that exploit the TCP three-way handshake. Traditional SYN cookies mitigate half-open connection exhaustion but may exhibit limited replay resistance under certain adversarial conditions. This paper presents a nonce-enhanced, HMAC-SHA256-based SYN cookie mechanism designed to strengthen handshake validation while preserving stateless operation. The implemented framework binds each connection attempt to a time-bounded, per-session nonce and embeds a truncated HMAC within the TCP sequence number field. The mechanism is implemented and experimentally evaluated using a custom-built simulation framework, NOxSYN. Under concurrent SYN flood conditions, the enhanced design successfully validated legitimate handshakes while maintaining stable operation under adversarial load. Measured server-side cryptographic processing remained below 1 ms per connection, with stable CPU utilization during testing. These results demonstrate that nonce-based replay protection can be integrated into a SYN cookie framework while preserving scalability and stateless operation. The current evaluation focuses on implementation-level validation and performance characterization, providing a foundation for future security-oriented assessment across a broader range of replay-based attack scenarios. Full article

Frequent engineering changes in manufacturing require worker instructions to be updated quickly and reliably. In many production environments, however, update handling still depends on manual comparison procedures, delayed communication, or repeated traversal of large document collections, limiting responsiveness during ongoing production changes. This paper presents a hierarchical hash-based method for change detection in structured manufacturing documents as the computational core of a worker assistance system for near-real-time instruction updates in the context of in-line qualification. Heterogeneous instruction data are transformed into canonical hierarchical document structures, from which SHA-512 digests are generated at multiple structural levels. During repeated comparison operations, document-state evaluation is reduced to digest comparison, while structural differences can be localized through hierarchical refinement of affected substructures. The method is integrated into a system architecture that combines predecessor-linked version management with role-specific filtering for controlled dissemination of relevant instruction updates. The approach was implemented in an automotive assembly use case involving structured work instructions and evolving production documentation. The evaluation demonstrates that the proposed approach reduces repeated comparison effort relative to conventional field-wise traversal methods while maintaining the ability to localize structural changes through hierarchical refinement. The reported results focus on computational behavior and implementation feasibility in structured manufacturing environments rather than hardware-specific throughput benchmarks. Overall, the results indicate that hierarchical comparison of structured instruction states provides a practical basis for change-aware worker assistance and controlled propagation of instruction updates in evolving manufacturing environments. The evaluation focuses on repeated-comparison scenarios in structured manufacturing settings and does not address semantic interpretation of detected changes or large-scale distributed deployments. Full article

SQL Injection is a well-known vulnerability that has persisted in web applications for decades. A widely held assumption among developers is that even when SQL Injection is present, hashing or encrypting sensitive data using SQL-provided cryptographic functions, such as sha256() or md5(), renders stolen data unrecoverable. This paper challenges that assumption directly. We demonstrate that invoking cryptographic functions within SQL statements does not protect plaintext credentials against an attacker who already has SQL Injection access, not because the hash functions are weak but because their plaintext arguments are transiently exposed in DBMS in-memory monitoring views before the hash function executes. We exploit this window using a technique that we call SQLSnoop, which repurposes built-in SQL looping constructs to poll the monitoring view at high frequency within a single injected statement. We demonstrate SQLSnoop against four major RDBMS platforms: MySQL, MSSQL, Oracle, and PostgreSQL. Systematic quantitative evaluation is conducted on MySQL, while feasibility on MSSQL, Oracle, and PostgreSQL is confirmed through working Proof-of-Concept implementations against each platform’s respective in-memory monitoring view. Our evaluation on MySQL shows attack success rates consistently above 90%, reaching 100% at 1.2 or more virtual CPU cores, and holding across all four Data Manipulation Language operations. The key practical implication is that SQL-layer hashing is fundamentally insufficient as a defense against SQL Injection, and sensitive data must be hashed at the application layer before the SQL statement is constructed. Full article

Background/Objectives: Strategies to improve the Social Health Authority (SHA)’s equity can be identified by analyzing the Kenya Demographic and Health Survey (KDHS) 2022. This study reports evidence of determinants of health insurance coverage in Kenya. Methods: Household- and individual-level datasets from the Kenya Demographic and Health Survey conducted between February and July 2022 were combined to form the analyzed dataset. Proportions of individuals with and without health insurance were estimated. The associations between potential determinants and health insurance status were calculated using the Rao–Scott chi-square. Logistic regression was used to analyze the determinants of health insurance coverage. Results: Most of the 14,232 participants were literate (75%), relatively poor (56%), in good health (79%), connected to electricity (55%), and radio listeners (61%). About 34% had health insurance, with 93% of the insured covered by the NHIF. Twenty predictors (Adjusted F = 4.2–434.1, p < 0.0001) were included in the complex sample logistic regression model, but only nine were statistically significant predictors of health insurance coverage. The key predictors were education level; wealth index; ownership of a solar panel, television, smartphone, and computer; age; and recent outpatient care (11–80% differences in odds). Conclusions: Health insurance coverage remains low in Kenya due to low education levels, poor economic status, and disparities in access to media. The SHA can emphasize media campaigns in the informal sector to increase premium payments. Accelerating socioeconomic advancement and adopting tax-based funding could speed up Kenya’s progress towards UHC. Full article

Steelmaking produces large volumes of slag, a by-product with environmental risks due to accumulation and possible contamination. This study explores its use as a mineralizing agent in chamotte refractories. Slag rich in clinoferrisilite was added up to 5 wt.% to partially replace fine chamotte. Samples were shaped by semi-dry pressing and fired at 1350 °C. Chemical and phase composition, thermal behavior, microstructure, and physico-mechanical properties were analyzed. Results showed slag addition increased mullite content to 68 wt.% and promoted secondary magnesium–aluminosilicate phases (indialite, cordierite), indicating activation of reactions in the MgO-Al₂O₃-SiO₂ system. DSC and TGA revealed thermal effects between 1298 and 1325 °C, confirming slag’s fluxing role and lowering the liquid-phase sintering temperature. Optimal properties were achieved with 5% slag and 10% clay, yielding compressive strength of 24 MPa and apparent density of 2.30 g/cm³, meeting GOST 390-96 requirements for grade SHA. However, excess liquid-phase components reduce thermal stability. Thus, steelmaking slag is an effective secondary raw material, enhancing mullitization and refractory performance when used within controlled limits. Full article

Federated learning (FL) enables distributed model training in IoT environments while keeping raw data on local devices. However, protecting model-update exchange is difficult on microcontroller-class devices due to strict latency, memory, and energy constraints. Existing studies often evaluate lightweight cryptography outside complete FL pipelines or on more powerful hardware, leaving its practical overhead on MCU-class devices insufficiently explored. This paper presents an end-to-end, hardware-validated secure framework for exchanging model updates in federated learning on resource-constrained IoT microcontrollers. Implemented on ESP32-based edge devices, the framework combines lightweight block ciphers (SPECK, SIMON, and PRESENT), HMAC-SHA256 for integrity verification, and ECDH-HKDF for session-key establishment. The evaluation assessed latency, throughput, RAM/ROM footprint, and energy consumption. Results show that SPECK provides the lowest overhead (0.13 µs/byte, 8.68 MB/s, 138.3 mJ), SIMON offers intermediate performance (0.41 µs/byte, 1.96 MB/s, 184.9 mJ), and PRESENT incurs the highest computational cost (89.37 µs/byte, 0.011 MB/s, 446.2 mJ). In the CICIoT2023 federated intrusion-detection evaluation, the secure model maintained stable convergence and achieved 85.43% accuracy after 20 rounds, remaining close to the centralized baseline. These findings demonstrate the practical feasibility of secure model-update exchange in FL on real IoT microcontrollers and provide hardware-grounded guidance for cipher selection under tight resource budgets. Full article

The Internet of Things increasingly relies on EDHOC (Ephemeral Diffie–Hellman Over COSE, RFC 9528) and OSCORE (Object Security for Constrained RESTful Environments, RFC 8613) for lightweight authenticated key exchange and application-layer security. On resource-constrained devices, however, the computational cost of these protocols remains prohibitive in software: a complete EDHOC handshake requires hundreds of milliseconds to several seconds on typical embedded processors. Prior evaluations of EDHOC and OSCORE focus almost exclusively on ARM Cortex-M platforms; to the best of our knowledge, no dedicated evaluation or hardware acceleration study exists for RISC-V. This paper presents the first performance characterization of EDHOC and OSCORE on a RISC-V platform. It introduces a hardware accelerator integrated as a memory-mapped peripheral within a Rocket RV32IMAC SoC. The accelerator offloads the complete EDHOC Method 3 handshake, encompassing X25519 scalar multiplication, HMAC-SHA-256 key derivation, AES-CCM-16-64-128 authenticated encryption, and all protocol state management and message construction within a single hardware boundary; OSCORE per-packet AEAD is accelerated through a dedicated post-handshake interface using the same core. By moving the entire handshake execution to dedicated hardware, the accelerator eliminates the residual overhead that remains in software, regardless of whether individual cryptographic primitives are offloaded. Implemented on a Xilinx Arty A7-100T FPGA, the design consumes 10,597 Slice LUTs, 10,421 Slice Registers, and 15 DSP slices. The accelerator completes the EDHOC handshake in 6.64 ms and 4.52 ms for the Initiator and Responder, respectively, achieving 58× and 85× speedups over the optimized Monocypher software baseline on the same platform, and delivers 37× to 56× speedups for OSCORE per-packet AEAD acceleration across payload sizes from 10 to 1000 bytes. The host firmware footprint is reduced from over 25 KB to 3.6 KB for EDHOC-only and to 5.2 KB for the combined EDHOC and OSCORE stack. Full article

With the rapid deployment of deep neural networks (DNNs) on edge devices, traditional hardware accelerators face significant challenges in terms of data security, computational redundancy caused by sparsity, and uneven utilization of on-chip resources. This paper proposes SaE-FPGA, a secure and efficient DNN accelerator designed specifically for edge FPGA platforms. The architecture introduces three core innovations: (1) Hash-Bypass Processing Unit (HBPU): Integrating a high-speed SHA-256 hardware engine with a hash-sparse bitmap mechanism, it enables real-time data integrity verification within a single clock cycle while skipping computations for redundant zero-value data. (2) Flexible Mixed-Precision Processing Element (FMP): By reconfiguring idle BRAM and LUT resources into an active lookup table multiplication engine, it overcomes the physical bit-width limitations of DSP blocks and supports INT8/INT6/INT4 mixed-precision multiplication. (3) Multi-mode Reconfigurable Streaming Frame (MRSF): A sparse-aware, elastic load balancing and data routing mechanism designed to mask long memory access latencies and ensure high hardware resource utilization. Experimental results on the Zynq 7045 platform demonstrate that SaE-FPGA reduces redundant computations by 23.2% while maintaining high precision and minimizing precision loss. The system effectively mitigates the risk of physical tampering. When tested on ResNet-50, it achieved a 27.2% improvement in energy efficiency and a 2.97× speedup compared to DSP-based FPGA solutions. Furthermore, by fully exploiting the hybrid BRAM-LUT and DSP configuration, the proposed accelerator achieves a remarkable peak throughput of 782.4 GOPS. Full article