Search Results (1,491)

Educational information systems have evolved into highly interconnected digital landscapes that support learning management platforms, student information systems, institutional repositories, and online assessment environments. As these systems increasingly operate across cloud infrastructures and mobile devices, ensuring the confidentiality, integrity, and availability (CIA Triad) of educational data is critical for safeguarding institutional operations and maintaining trust in digital education services. This paper investigates how next-generation security protocols, such as adaptive multi-factor authentication and advanced access control and data protection mechanisms, can reinforce ISO/IEC 27001:2022 requirements within contemporary educational information systems. The analysis maps emerging protocol capabilities to relevant new ISO/IEC 27001:2022 control domains, illustrating how they mitigate threats associated with unauthorized access, data manipulation, and service disruption. The proposed framework is further supported by an implementation-oriented mapping and an illustrative operational architecture that demonstrates the feasibility of translating prioritized security determinants into practical mechanisms. The FAHP analysis identifies access control mechanisms, backup and recovery, and data validation as the three highest-weighted determinants, with aggregate weights of 0.061, 0.059, and 0.057, respectively. These determinants are translated into a determinant-driven Security Operationalization Matrix that connects ISO/IEC 27001:2022 control domains, CIA dimensions, and technology recommendations, and is complemented by implementation feasibility considerations tailored to the budgetary, infrastructural, and resource constraints characteristic of educational institutions. Based on the prioritization results and conceptual operationalization, the proposed integrative approach provides a structured and progressively adoptable foundation for CIA-oriented security governance in digital educational environments. Full article

Purpose-aware AI systems are increasingly deployed in safety-critical, multi-agent, and human-facing environments, where they must transform heterogeneous data into timely, explainable, and goal-aligned decisions under uncertainty. Existing architectures often couple perception, reasoning, communication, and security only at the pipeline level. This creates a research gap in unified semantic transformation, purpose-oriented judgment, bounded imperfection handling, and semantic self-protection. To address this gap, this paper proposes a DIKWP+BUG semantic–cognitive reference architecture for artificial-consciousness-oriented computing, without claiming definitive artificial consciousness. The architecture represents cognition through the Data–Information–Knowledge–Wisdom–Purpose (DIKWP) model and uses BUG theory to model bounded approximation, incomplete evidence, and confidence miscalibration in cross-dimensional reasoning. The model is mapped to an Artificial Consciousness Processing Unit (ACPU) reference substrate, an Artificial Consciousness Operating System (ACOS), a DIKWP semantic communication subsystem, and a concept–semantic fused security subsystem. The components are implemented through runtime emulation and evaluated in smart-city governance, autonomous-driving, and medical-triage simulations. Compared with selected baselines, the prototype increased cognitive throughput from $4.5\mathrm{k}$ to $7.8\mathrm{k}$ logged events, reduced perception–action latency from $340\mathrm{ms}$ to $120\mathrm{ms}$, reduced CPU utilization from $95\%$ to $68\%$, lowered smart-city congestion duration by $30\%$, improved emergency response time by approximately $40\%$, achieved 0 collisions versus approximately $2/10$ baseline IoV runs, and improved medical-triage accuracy from $85\%$ to $92\%$. These online-runtime results provide initial feasibility evidence under controlled simulation conditions; they do not include offline model-preparation costs and therefore should not be interpreted as end-to-end lifecycle speedups. Matched-compute ablation, statistical benchmarking, hardware prototyping, and real-world validation remain future work. Full article

Soil erosion (SE) is a constant, complex land degradation process, a common natural disaster that occurs all over the world and severely impacts soil fertility, food security, and environmental balance. Soil erosion depends on many factors, including soil properties, slope, vegetation, rainfall amount and intensity, and anthropogenic activities. There are two main natural erosive forces by which soil is eroded and transported—water and wind. Water erosion refers to the detachment, transportation, and deposition of soil particles (solid runoff) into river networks. These particles, varying in size and composition, are the main products of soil erosion and most strongly affect river ecosystems. Solid runoff, or sediment-laden runoff, affects water quality, destroying habitats, carrying pollutants, reducing reservoir storage, and causing flooding. Erosion control activities also influence river ecosystems in different ways. Hydrotechnical facilities, a major erosion control practice, can alter the composition of aquatic biota by disrupting longitudinal connectivity and isolating populations. Reforestation and afforestation are other erosion control practices that have a strong impact on ecosystems. Stormwater retention systems in urban and forest areas are also important measures addressed in this review. This review examines complex environmental interactions and the roles of erosion and erosion control activities in river ecosystems. During the research, several key points were established: erosion and erosion control activities significantly affect river ecosystems. There is a lack of quantitative analysis of erosion intensity and its influence on ecosystems. This is probably due to the exceptional complexity and diversity of river ecosystems, but such a study would provide important information about complex relationships in nature. Full article

Quantum key distribution (QKD) networks require relaying when distant key management entities share no direct quantum link. Most relay strategies, however, rely on centralized control or globally maintained routing state. This paper asks whether useful security and efficiency can still be obtained with topology-oblivious stochastic forwarding. It studies the security-overhead trade-off in a model in which fragmented key material is relayed via random-walk variants and reconstructed under privacy amplification. The analysis asks whether strictly local forwarding can retain useful information-theoretic security (ITS). Evaluation on the GÉANT topology, representing a European academic backbone network, shows clear differences between random-walk variants. The proposed highest-score-neighbor local path-diversification heuristic reduces the probability that relayed key material passes through a compromised node. The evaluation also shows that scouting-based loop erasure significantly shortens sampled routes and improves throughput in the model. Against one- to three-node cartels, random flow protects slightly more source–target pairs than a static disjoint-multipath method on the evaluated topologies. These findings position topology-oblivious stochastic forwarding as a simpler decentralized design for QKD relaying without centralized orchestration or gossip protocols. Full article

Unmanned aerial vehicle (UAV) red–green–blue–infrared (RGB-IR) object detection is important for traffic monitoring, security surveillance, and urban management, but remains challenging because aerial targets are often small, densely distributed, and affected by complex backgrounds. In addition, RGB and infrared (IR) modalities contribute unequally under different imaging conditions, making simple feature concatenation or indiscriminate middle-layer fusion insufficient for stable cross-modal utilization. To address this problem, this paper proposes Selective Interaction Mechanism and Prefiltering Complementary Spatial Refinement (SIM-PCSR), a key-layer complementary enhancement method for UAV RGB-IR small-object detection. The proposed method decomposes cross-modal modeling into two stages. SIMAdapter first performs selective interaction on the small-object-sensitive P3 layer before fusion, suppressing redundant responses and enhancing potentially complementary modal evidence. PCSR then refines the fused representation through prefiltering, modal selection, and local window residual refinement, injecting reliable complementary information into the key-layer fused feature in a controlled manner. Experiments on the DroneVehicle dataset show that SIM-PCSR achieves 85.323 mean average precision (mAP)₅₀ and 63.572 mAP_50:95, improving the Fixed Middle Fusion baseline by 0.523 and 0.751 percentage points, respectively. These gains correspond to relative improvements of 0.62% and 1.20% over the baseline. Module ablation, position ablation, repeated-seed evaluation, category-wise analysis, scale-wise analysis, and qualitative visualization jointly demonstrate that explicit selection and organization of cross-modal information can improve UAV RGB-IR small-object detection under modality imbalance and background interference. Full article

The counterfeiting of official documents and banknotes represents a critical threat to global security and requires robust and low-cost protection techniques. This work presents an innovative information security system that uses photoluminescent inks for chromatic multiplexing of QR codes. Unlike conventional cryptographic methods, the proposed approach employs physical-layer information hiding through the superposition of two QR codes encoded in magenta and cyan colors on a white background. The controlled interaction between these codes generates an additional logical state that enables a third representation of information through pixel-level operations. The resulting chromatic QR code remains visually imperceptible under ambient illumination and can be reliably recovered through chromatic demultiplexing and thresholding process. Additionally, its visibility can be enhanced under ultraviolet (UV) excitation due to photoluminescent behavior and spectral response variations. The experimental results demonstrate that both encoded data layers can be extracted independently with high fidelity using standard CMOS sensors, while preserving structural integrity and decodability. The proposed scheme increases information density within a single optical tag while improving resistance against unauthorized replication and visual forgery. Full article

Urban low-altitude unmanned aerial vehicle (UAV) logistics networks face critical operational bottlenecks due to complex three-dimensional spatial blockages, continuous communication diffraction, and severe vulnerability to information-layer threats such as Automatic Dependent Surveillance—Broadcast (ADS-B) signal anomalies. To address these interconnected challenges, this paper proposes an event-driven, cloud–edge collaborative digital twin framework to guarantee continuous multi-link communication and flight safety. The architecture operates through a dual-tier “Teacher–Student” paradigm. Under secure conditions, a cloud digital twin acts as a high-capacity “Teacher,” employing Density-Based Spatial Clustering of Applications with Noise (DBSCAN) to partition heterogeneous user topologies. It then utilizes an energy-guided stochastic diffusion sampling (EGSDS) method to refine initial macroscopic routing, generating precise, outage-free global trajectories by systematically minimizing non-line-of-sight (NLoS) observation penalties and kinematic regularization costs. To counteract signal anomalies, a distributed Time Difference of Arrival (TDOA) anchor network continuously validates UAV coordinate integrity. If a threshold is breached, control authority is instantly transferred to the UAV’s edge digital twin. This resource-constrained edge tier relies on a localized “Student” network trained via progressive distillation. By compressing the computationally heavy iterative diffusion process into a rapid one-step inference model, the UAV autonomously generates a secure, short-range emergency path that strictly adheres to minimum communication thresholds. Once interference clears, the cloud seamlessly regains control to complete the logistics mission. Experimental results demonstrate that the proposed scheme significantly outperforms conventional heuristic routing methods in cloud-based scenarios. Furthermore, the edge-based distillation mechanism substantially improves the overall trajectory survival rate under signal anomalies, ensuring resilient and continuous logistics operations. Full article

We study whether the semantic category of a hidden display terminal can be inferred from a wall-mediated indirect observation when the display remains outside the camera field of view under a controlled and calibrated scene configuration. This setting provides a security-motivated feasibility test for indirect optical semantic leakage, but it remains challenging for two reasons. First, indirect propagation makes the wall pattern dominated by the occluder contour, while category-bearing evidence survives only as weak radiometric variations, making stable extraction difficult. Second, even after front-end recovery, low-frequency support is relatively stable, whereas the mid- and high-frequency details required for class separation remain weak and distortion-prone; as a result, the classifier may drift toward dominant but weakly informative coarse-grained patterns and fail to consistently accumulate fine-grained discriminative cues. We propose SCISA-Net, which combines scene-constrained inversion with multi-stage Haar-subband attention to reorganize indirect observations, compensate residual feature degradation, and aggregate class-relevant subband evidence. Experiments on a paired 31-class benchmark show stable recognition, robustness to illumination attenuation and ambient background interference, matched scene-operator re-parameterization capability, and clear degradation when key inverse or subband components are disrupted. These results support the feasibility of category-level semantic inference from calibrated wall-mediated indirect observations. Full article

Recently, there has been an increasing prevalence of software supply chain attacks on software component suppliers. These attacks have targeted suppliers with relatively weak security or they have exploited vulnerabilities in open-source software. The software bill of materials (SBOM) has gained significant attention as a mechanism for improving software supply chain transparency and traceability. In this study, we propose an SBOM distribution architecture based on Hyperledger Fabric, which is a permissioned blockchain platform, to facilitate secure SBOM management. This approach utilizes Hyperledger Fabric private data collections (PDCs) to separate SBOM metadata from sensitive component information, thereby enabling confidential data sharing while reducing the blockchain storage overhead compared to a fully on-chain approach. The proposed PDC-based architecture achieves lower latency and higher throughput than the fully on-chain approach under the evaluated workload conditions, while supporting integrity verification and controlled sharing of sensitive component data. Full article

Due to the indispensable use of the internet, malicious actors have exploited URLs as a threat source of network information security and integrity. URL detection based on traditional methods has become inefficient against the uncontrolled increase of URLs, especially when facing dynamic and large-scale threats. To address the limitations of traditional methods and to provide intelligent and scalable detection of malicious URLs, this study proposes the hybrid framework (NetGuard) by integrating probabilistic data structures (PDSs) with machine learning (ML) capabilities. The proposed NetGuard utilizes PDSs to develop a Hybrid Scalable Detection Filter (HSDF), which combines the strengths of counting Bloom filters (CBFs) (deletion capability) and Scalable Bloom filters (SBFs). The proposed HSDF provides efficient membership queries under bounded false-positive rates (approximately 0.01) and ensures efficient data management and low-latency lookups on a scale of 10⁻⁵ s. On the other hand, NetGuard leverages the ML classifier capabilities to train and package a learned classifier for detecting malicious URLs. The proposed framework utilizes Decision Trees (DTs) and Random Forest (RF) classifiers. The proposed classifiers are trained by a novel SupURLsIdDs dataset which includes fifteen distinctive lexical and structural URL features extracted from four URL classes: benign, defacement, malware, and phishing URLs. The experimental results indicated the effectiveness of the HSDF in insertion and deletion operations, with minimal memory consumption (approximately 2.7 MB for 222,000 URLs) while maintaining a controlled false-positive rate (approximately 0.01 on Real-only subset up to 0.12 with synthetic data). The HSDF memory footprint represents a 99.88% enhancement compared to the RF model (which demands 2253.17 MB); thus, the HSDF complements RF as an ultra-lightweight first line of defense. The ML classifiers showed the superiority of RF, which achieved an overall classification accuracy of approximately 96% on large-scale URL data. These experiments are conducted using benchmark datasets constructed from aggregated real and synthetic data to demonstrate the scalability, adaptability, and resource efficiency of the first phase of NetGuard as a practical foundation for real-time web threat detection. The real-time integration and dynamic updates are presented as a deployment architecture and constitute future work. Full article

Modern distribution networks increasingly face operational stress from variable demand and high penetration of distributed energy resources, challenging the adequacy of purely reactive protection schemes. This study addresses this challenge by enhancing a developed adaptive protection software platform with a Geographic Information System (GIS) driven predictive load forecasting capability to enable anticipatory protection coordination. The proposed framework integrates spatially resolved demand modeling, regulatory and planning constraints, and machine learning-based short- to medium-term load forecasting with a relay coordination and optimization engine. Forecasted load profiles are used as inputs to an optimization layer that proactively updates relay pickup and time delay settings to maintain selectivity and system security under predicted operating conditions. The approach is validated at laboratory scale using real Intelligent Electronic Devices (IEDs) interfaced with synthetic GIS-based network and load datasets. Experimental results indicate that incorporating forecast-informed settings improves coordination margins and reduces the risk of relay maloperation compared with reactive adaptive protection alone. The findings demonstrate that coupling GIS based constrained load forecasting with adaptive relay control can enhance protection performance in active distribution networks, supporting more resilient and forward-looking protection strategies. Full article

Federated learning offers a promising approach for cross-institutional financial risk control modeling but encounters two key challenges in practice: feature space heterogeneity and low sample overlap rate. Current federated transfer learning methods often rely heavily on sufficient overlapping samples or explicit feature alignment. However, these approaches frequently result in negative transfer when enforced alignment is applied in highly heterogeneous environments. To address this issue, we propose FedVI, a novel federated transfer learning framework that integrates tabular-to-image conversion and meta-learning mechanisms. Moving beyond conventional methods that rely on sample-level alignment, FedVI employs a federated dual-stream feature alignment strategy to securely reconstruct a unified global feature map across institutions. Subsequently, FedVI integrates federated Image Generator for Tabular Data (IGTD) with tabular Transformer technology to convert one-dimensional tabular data into two-dimensional visual-semantic tensors. These tensors effectively fuse spatial topology and semantic information while embedding an independent Mask channel to explicitly retain the true missingness patterns of features. Finally, FedVI adopts the Model-Agnostic Meta-Learning (MAML) architecture to facilitate global parameter optimization. We evaluated FedVI on the real-world Lending Club credit dataset and Home Credit Default Risk datasets under highly heterogeneous federated settings (i.e., heterogeneous feature spaces across three clients and scarce overlapping samples). The results reveal that FedVI achieves competitive performance against advanced baselines such as FedProx, FedRep, and FedKT, particularly in recall and F1-Score. These findings indicate that FedVI can effectively support cross-domain adaptation under heterogeneous federated learning settings. Full article

Nonlinear memristors frequently contribute to enhancing the dynamical richness of chaotic systems, yet their complexity and flexibility have often been overlooked. In this work, a piecewise non-smooth threshold memristor model is proposed, which is coupled as a nonlinear term into the Sprott C system, yielding a novel four-dimensional memristive chaotic dynamical system. From a theoretical perspective, stability analysis reveals that unstable index-2 saddle-focus equilibrium points are governed by the memristive piecewise parameter, and the topological invariance of the system is verified. In numerical simulations, bifurcation diagrams, Lyapunov exponents, and phase portraits are employed to reveal the mechanism of novel tunable chaotic dynamics. The results demonstrate that memristive coupling strength can induce the system to generate double-scroll, double-wing, and double-butterfly chaotic attractors; the piecewise parameter of the memristor can control the system to produce multi-structure attractors with expanded quantity, and the initial condition of the memristor can regulate the system to generate offset-boosted chaotic attractors. Finally, the novel tunable dynamics is applied to water facility image encryption. Experimental results demonstrate that the proposed algorithm possesses a key space of $2^{100}$, a correlation coefficient of only 0.0002, and information entropy close to the ideal value of eight. The NPCR and UACI reach 99.6161% and 33.4669%, respectively, the key sensitivity is up to ${10}^{-16}$, and all p-values from the NIST tests are greater than 0.01, confirming that the algorithm achieves excellent security performance. Full article

Evaluating intrusion detection methods at the level of individual MITRE Adversarial Tactics, Techniques, and Common Knowledge (ATT&CK) for Industrial Control System techniques requires Operational Technology traffic in which each attack sequence carries its MITRE technique identifier as ground truth. Publicly available Industrial Control System datasets either provide coarse attack-versus-benign labels (SWaT, WADI, CIC-APT-IIoT) or require ex-post technique reconstruction from CALDERA operation logs, and therefore do not support per-technique benchmarking. We describe one primary contribution and two supporting contributions, demonstrated on one Modbus/Raspberry-Pi programmable logic controller/CALDERA/convolutional bidirectional Long Short-Term Memory autoencoder (CNN-BiLSTM-AE) use case. The primary contribution is an in-orchestrator labelling methodology for per-technique-labelled Industrial Control System attack capture. Its single load-bearing property is that the campaign orchestrator owns the label primitive and writes each per-sequence technique identifier into the capture artefact at injection time, eliminating ex-post log-to-packet alignment. The first supporting contribution is a protocol-aware detection pipeline. Its load-bearing architectural choice is a priority-ordered protocol router that dispatches each labelled flow to a per-protocol detector plug-in (protocol-aware features here, with generic-flow features admissible as an alternative plug-in policy on the same router). The second supporting contribution is a suite of four reproducible CALDERA chains (three Information-Technology-to-Operational-Technology kill chains plus one enterprise-side control) that exercise the labelling methodology end-to-end and the detection pipeline along complementary detection paths. All three contributions are platform-independent: any ATT&CK-aligned emulator and any fieldbus protocol can host the labelling methodology, and any detector trained on an admissible feature space can plug into the router. The dataset contains 40,000 benign and 9997 attack Modbus sequences spanning four ATT&CK techniques (T0802 Automated Collection, T0831 Manipulation of Control, T0836 Modify Parameter, T0846 Remote System Discovery). On this dataset, the CNN-BiLSTM-AE reaches a 100% true-positive rate (TPR) at the 98th-percentile benign threshold across all four techniques and a 99.7% overall TPR at the tighter 99.5th-percentile threshold, with per-technique TPR between 96.1% (T0836 Modify Parameter) and 100% (T0802 Automated Collection, T0846 Remote System Discovery). Across the four CALDERA chains, the Modbus autoencoder produces 234 protocol-layer detections and the Security Information and Event Management (SIEM) rule set produces 30 alerts, with per-chain tactic coverage between 0.714 and 0.786 and CALDERA-ability success rates between 0.800 and 0.857. Full article

To combat counterfeiting in down products and enhance enterprise credibility through technical means, this paper proposes a blockchain-based down quality traceability framework named DPT (down-product traceability). Built on Hyperledger Fabric, the framework integrates the InterPlanetary File System (IPFS), digital anti-counterfeiting watermarks (DW), QR codes, and category-specific encryption strategies to establish a trusted data chain throughout the entire supply chain. Role-based access control (RBAC) is adopted to ensure the secure submission and query of traceability information by all supply chain participants. A trinity data storage architecture is designed to achieve secure and efficient data management. A full-fledged application system was developed and deployed in cooperation with a leading down products enterprise to validate its practical applicability. Performance evaluation using Hyperledger Caliper 0.6.0, which focuses on throughput, latency, and resource utilization under stress testing, confirms that the DPT framework meets the requirements of real-world production. Furthermore, practical sales data verify that the proposed system effectively enhances consumer trust and mitigates counterfeiting behaviors in the market. Future work will focus on further optimizing write operation performance and evolving the system into a more robust clustered architecture. Full article