Search Results (4,695)

This article examines two reports dated 4 October 1897 and 6 January 1898 written by the Ottoman military attaché in Madrid, Reşid bin Galib, Staff Senior Captain (Kolağası), to analyze how the late Ottoman Empire interpreted the Spanish military structure and the insurrection in Cuba. Situated within the broader development of nineteenth-century military intelligence practices, the study employs textual and contextual analysis, focusing on institutional language, strategic categorization, and threat perception. The report dated 4 October 1897 provides a detailed account of the military-administrative organization in Cuba, including command hierarchy, troop distribution, logistical infrastructure, and internal security mechanisms, while the report dated 6 January 1898 evaluates the historical trajectory of the rebellion and offers a comparative assessment of combat- and disease-related casualties, highlighting the importance of logistical and administrative capacity in warfare. Taken together, these documents show that Ottoman military intelligence systematically monitored a colonial crisis beyond Europe and interpreted it through an institutional military framework. The reports also reflect late Ottoman concerns regarding external intervention, security, and imperial stability. By examining a non-European colonial conflict, the article demonstrates how military knowledge was transferred, reframed, and integrated across imperial contexts, thereby contributing to the historiography of Ottoman military attachés and highlighting their role in shaping the Empire’s global strategic awareness at the turn of the twentieth century. Full article

Quantum secure direct communication, as an important branch of quantum communication, possesses strict information-theoretic security and can achieve secure communication in channel environments with noise interference and eavesdropping threats. As voice communication is the most fundamental and widespread communication method in daily life, guaranteeing its security and efficiency has become an important research topic in current communication technology. One-way quantum secure direct communication technology can build an efficient and reliable security barrier for voice communication services, effectively preventing the leakage of private information in voice communication. This paper proposes a duplex voice communication scheme based on one-way quantum secure direct communication. By adopting a method combining multi-task parallel processing and stream processing, the communication rate and transmission delay performance of the system are significantly improved. Relying on quantum secure direct communication technology and the one-time-key encryption channel within the system, duplex voice communication is achieved securely. The real-time temperature drift compensation algorithm is introduced to ensure the long-term stable operation of the system. At the same time, through the real-time temperature drift prediction mechanism, the strategy selection during the call process is optimized to ensure the quality of the voice communication. To verify the feasibility and performance of this scheme, a one-way quantum secure direct communication duplex voice communication system was built in the laboratory environment, and comprehensive performance indicator tests were conducted. The test results show that the constructed one-way quantum secure direct communication system can fully meet the performance requirements of duplex voice communication. The realization of this system successfully achieves the goal of secure and efficient quantum voice communication, laying an important technical foundation for further expanding the practical application scenarios of quantum communication technology and promoting the industrialization development of quantum communication. Full article

The increasing deployment of IoT-enabled electric-vehicle charging networks has created a rapidly evolving cyber–physical environment in which security mechanisms must operate amid ever-changing data patterns and resource constraints. In these environments, static Machine Learning (ML) pipelines are often insufficient because they struggle to adapt to concept drift issues, emerging attacks, and real-time operational requirements. We analyzed cybersecurity vulnerabilities, challenges of conventional ML approaches, and the possibilities of AI-powered, adaptive security measures. This paper examines Online AutoML and its advantages, including automated adaptation to streaming data, reduced human intervention, and privacy-preserving, resource-aware learning. Furthermore, this paper discusses adversarial attacks and defences in Online AutoML systems, highlighting the need for frameworks that jointly address concept drift, scalability, privacy, and adversarial threats. Finally, this study emphasizes the importance of establishing comprehensive public benchmarks for Online AutoML research. Full article

Linearly homomorphic signatures (LHSs) are widely used in scenarios such as network coding and the Internet of Things, but their security faces the serious threat of key leakage. To address this issue, this paper introduces a forward secure mechanism into LHSs, aiming to construct a linearly homomorphic signature (LHS) scheme that can resist the risk of key leakage. By combining the binary tree minimal cover set mechanism with lattice-based extension algorithms, we construct an LHS scheme that supports time-period key updates. We prove its forward secure unforgeability under the standard model (SM) by reducing it to the Short Integer Solution (SIS) problem. To the best of our knowledge, this scheme is the first provably secure lattice-based forward secure linearly homomorphic signature (FSLHS) scheme in the SM, filling a theoretical gap in existing research. Furthermore, we apply this scheme to a smart grid data acquisition system and verify its practicality through concrete performance analysis. Full article

Soil erosion poses a severe threat to agricultural productivity and ecological security on the Loess Plateau. However, previous studies have rarely integrated physical modeling, elasticity coefficients, and spillover effects into a unified framework at the county level. To address this gap, this study coupled the Revised Universal Soil Loss Equation (RUSLE) with the Spatial Durbin Model (SDM) to systematically investigate the spatiotemporal dynamics, factor elasticity characteristics, and spatial dependence mechanisms of soil erosion on the Loess Plateau from 1990 to 2024. Results show that the annual average erosion rate decreased by 15.5%, with a highly volatile phase before 2001 and a stabilized, low-erosion phase thereafter. The driving factors exhibited marked heterogeneity in direction and strength. The land cover and management factor (C) was the strongest erosion-reducing factor, whereas annual precipitation (PRE) was the primary natural erosion-enhancing factor. County-level erosion also displayed significant positive spatial dependence. PRE had a stable positive indirect effect, whereas C and the support practice factor (P) mainly contained erosion within local jurisdictions. These findings of a unified RUSLE–SDM framework reveal a joint driving mechanism of localized human interventions and climate-driven cross-regional spillovers, providing quantitative support for differentiated soil and water conservation strategies on the Loess Plateau. Full article

(1) Background: Cybersecurity has grown in scale and complexity, increasing the need for shared conceptual frameworks that enable consistent, interoperable, and machine-readable representations of security knowledge. Ontologies address this need by structuring core cybersecurity concepts, yet existing efforts vary widely in purpose and methodological rigour. Prior developments tend to follow either an instrumental path—prioritizing usability and rapid adoption—or a formal path, emphasising logical precision and reasoning capabilities. This divergence has resulted in a fragmented landscape lacking analytical synthesis. (2) Methods: To clarify current practices and uncover research opportunities, we conducted a systematic literature review of 93 cybersecurity ontologies published over the past decade. Following PRISMA guidelines, we analysed their conceptual coverage, development methods, validation strategies, and alignment with the NIST Cybersecurity Framework (CSF) 2.0. (3) Results: Despite heterogeneity in scope, the ontologies consistently model core entities such as Asset, Threat, Vulnerability, Attack, and Countermeasure. However, conceptual coverage remains uneven: most contributions focus on the Identify and Detect functions of the NIST CSF, while Respond and Recover are largely underrepresented. This reveals a prevailing emphasis on preventive security rather than resilience and highlights gaps in empirical validation and industrial deployment. (4) Conclusions: The field shows strong conceptual maturation but limited methodological consistency and operational impact. Advancing cybersecurity ontologies will require integrating pragmatic and formal modelling traditions, incorporating emerging techniques such as knowledge graphs and LLM-assisted ontology learning, and expanding coverage toward post-incident response and recovery. These steps are essential for developing a unified, explainable, and adaptive cybersecurity knowledge base capable of supporting real-world security operations. Full article

The imminent threat of large-scale quantum computers motivates the deployment of post-quantum cryptography (PQC). CRYSTALS-Kyber, a leading lattice-based Key Encapsulation Mechanism, relies heavily on Number Theoretic Transform (NTT) operations, which remain a major performance and resource bottleneck. This paper presents a cross-platform NTT evaluation framework for CRYSTALS-Kyber, centered on an adaptive FPGA-based mixed-radix accelerator supporting radix-2, radix-4, and radix-8 configurations, together with comparative classical implementations and exploratory quantum-circuit prototypes. Classical evaluations show that an iterative Cooley–Tukey implementation outperforms a matrix-based baseline (≈3.6× faster for the forward NTT, ≈6.3× faster for the inverse NTT). Quantum prototypes implemented in Qiskit demonstrate proof-of-concept QFT-based NTT constructions under classical simulation environments, highlighting circuit-depth growth and noise sensitivity rather than practical hardware acceleration. The proposed FPGA design, based on a Xilinx Virtex UltraScale+ platform, employs an adaptive radix controller, LUT-based twiddle management, and Montgomery/Barrett modular arithmetic. Montgomery reduction provides superior timing and area trade-offs, with an estimated $F_{\max }$ of up to 231.48 MHz and only 5 DSPs for radix-2. At the same time, radix-2 offers the best resource/performance balance with a latency of approximately 32,804 cycles. The hybrid approach strikes a balance between near-term FPGA practicality and long-term quantum potential while preserving Kyber’s MLWE-based security. Experimental results and comparative analysis indicate that the adaptive design substantially reduces resource usage and timing overhead compared to recent HLS-based NTT accelerators. Full article

Industrial control systems (ICS), which manage critical infrastructure such as power grids and water treatment, are increasingly exposed to cyber threats and operational faults as their connectivity to external networks grows. AI-based anomaly detection has emerged as a key defense, yet three limitations restrict its practical deployment: (i) detected anomalies are treated uniformly without distinguishing between transient faults and intentional attacks, hindering tailored incident response; (ii) the trade-off between detection accuracy and the false-positive rate burdens experts with extensive manual triage and delays prompt action; and (iii) prevailing feature-attribution Explainable AI (XAI) techniques such as SHAP and LIME produce fragmented sensor-level explanations and fail to capture correlations among sensors in time-series data, undermining trust in model decisions. To address these gaps, this paper proposes a graph-based deep learning framework that (a) defines anomaly types in terms of the anomalous-sensor ratio measured before and after smoothing—which operationalizes the correlation-maintenance principle that faults keep coupled sensors jointly anomalous while attacks isolate them—enabling explicit separation of faults, attacks, false positives, and false negatives; (b) identifies ambiguous decisions near the detection threshold as candidate false alarms via dynamic threshold smoothing; and (c) provides correlation-aware graph visualizations for intuitive interpretation. Experiments on the Secure Water Treatment (SWaT) dataset center on this post-detection layer: built on a standard graph-based detector (F1-score 0.787 at Top-K = 10) that serves only as the substrate, the categorization separates faults from attacks, and the subsequent ambiguity analysis identifies false negatives with 83% precision and false positives with 73% precision. By separating attacks from faults and surfacing high-likelihood false alarms together with intuitive sensor-correlation explanations, the proposed approach reduces analyst workload and supports more reliable, prioritized incident response in ICS environments. Full article

The rapid progress of generative speech synthesis and voice-cloning technologies has enabled the creation of highly natural synthetic voices that pose a serious threat to telecommunication security. While most prior studies evaluate human ability to detect audio deepfakes using high-quality, studio-grade recordings, little is known about how real-world telecommunication channels affect perceptual detection. This study investigates the influence of three transmission scenarios—GSM (AMR-NB), VoLTE (AMR-WB), and VoIP with packet-loss modeling—on the human ability to distinguish natural speech from AI-generated speech. A custom speech corpus was developed, consisting of natural recordings from nine speakers and corresponding synthetic utterances generated using a state-of-the-art voice cloning system (ElevenLabs). All samples were processed through simulated telecommunication channels using real codec implementations. A listening test with 95 participants was conducted, involving binary classification (human vs. synthetic) and confidence ratings. Results show an overall detection accuracy of 54.8%, confirming that humans are poorly equipped to identify synthetic speech. Surprisingly, the highest accuracy was achieved for the narrowband GSM channel (63.7%), while VoLTE yielded the lowest performance (44.0%). The findings suggest that restricted bandwidth may emphasize prosodic irregularities typical of generative models, whereas high-quality channels mask synthetic artifacts, increasing susceptibility to voice spoofing. The results highlight the necessity of deploying additional security mechanisms in telecommunication systems relying on voice identity verification. Full article

We performed a cross-layer, system-level analysis of the post-quantum transition of blockchain architectures through a systematic review. The analysis, based on 108 peer-reviewed studies, moves beyond post-quantum cryptography (PQC) as merely a primitive substitution and examines how quantum pressures cascade through validation, propagation, interoperability, governance, and regulatory layers. Empirical results show that the authenticated payloads for lattice signatures grow from ~65–73 bytes (ECDSA) up to kilobyte-scale sizes, and verification overhead is increased by a factor of 2× to 5× depending on the deployment scenario. Such inflation can narrow block-capacity margins, increase propagation delay under fixed-interval regimes, and shift validator resource thresholds in heterogeneous networks. Moreover, the harvest-now–decrypt-later model creates a temporal asymmetry between the design options and the exposure window. These findings indicate that post-quantum resilience depends more on maintaining a structural balance among the tightly coupled technical and institutional stress channels than on the strength of the algorithm itself, and migration success ultimately depends on the ability to coordinate the management of these constraints, rather than on managing them separately. Full article

Land use and land cover change (LULCC) is increasingly recognized as a dominant driver of hydrological alteration in tropical watersheds, often exceeding the influence of climatic variability. This study evaluates the spatiotemporal dynamics of LULCC and their implications for hydrological sustainability in the Uberabinha River Basin, southeastern Brazil, between 1990 and 2020. Utilizing MapBiomas data and statistical analysis, the results reveal a marked expansion of mechanized agriculture, particularly soybean cultivation, which grew from 3426 ha to 54,162 ha, and urban areas, which expanded by approximately 89.4%. Conversely, natural vegetation and pasturelands decreased continuously, with pastures showing the sharpest absolute reduction, from 72,248 ha to 34,535 ha. Despite a 10.76% increase in annual precipitation between 1990 and 2020, the hydrological response exhibited a severe decline in streamflow, characterized by a 76.35% drop in minimum flow. Furthermore, the runoff index decreased from 0.0574 in 1990 to 0.0211 in 2020, indicating a critical loss in the basin’s capacity to convert rainfall into streamflow. These findings demonstrate a clear decoupling between precipitation and streamflow driven by LULCC, posing a severe threat to regional water security and highlighting the urgent need for integrated land–water management. Full article

Wireless Body Area Networks (WBANs) have become an essential component of modern Internet of Medical Things (IoMT) healthcare systems, enabling continuous monitoring of patient physiological signals through wearable sensors. Despite their advantages, WBAN environments remain highly prone to cyber threats, privacy breaches, and single points of failure. To address these risks, this work proposes a Hybrid Multi-Metric Anomaly Detection (HM-MAD) framework deployed on the NodeMCU-32S platform with BLE 5.0 connectivity for secure continuous glucose monitoring (CGM) data transmission. The detection model simultaneously analyses physiological signals, system-level parameters, and network-level communication metrics, enabling the reliable identification of multiple cyberattacks. The proposed system focuses on securing data transmission against relay attacks, where attackers induce communication delay without modifying payloads, potentially leading to false glucose readings, improper insulin dosage delivery, unauthorized control or denial-of-service. The Convolutional Neural Network (CNN) and Bi-Directional Long Short Term Memory (BiLSTM) model classifies attack types including timing manipulation, replay attacks, power glitches, firmware tampering, and sensor spoofing. Experimental evaluation demonstrates that the proposed CNN + BiLSTM framework achieves 94.6% detection accuracy with an average inference latency of 15 ms, representing a 50% latency reduction compared to Transformer-based intrusion detection models (30 ms), while simultaneously reducing computational overhead by 28% in terms of floating-point operations and memory utilization. These results indicate that the HM-MAD framework provides an effective and scalable solution for protecting resource-constrained IoMT healthcare systems against emerging cyber threats. Full article

Software-Defined Networking, as a relatively recent networking paradigm, offers centralized infrastructure management, flexibility and high programmability. However, it also creates particular security risks due to being exposed to external threats. To address these challenges, numerous methods have been developed and applied over the past few years. This study proposes a hybrid Intrusion Detection System that combines signature-based analysis with deep learning-based anomaly detection. In this architecture, a signature module quickly filters known attack patterns, while remaining traffic is analyzed by an autoencoder and a supervised deep neural network classifier. The final decision is based on rule-based prioritization of the outputs from both models, improving the reliability and robustness of detection. Full article

Cyber-physical systems (CPSs) based on the Internet of Things (IoT) form the backbone of modern smart infrastructures, including smart cities, healthcare monitoring, industrial automation, and intelligent transportation. However, connecting many resource-limited IoT devices makes them more vulnerable to cyber threats, particularly quantum attacks. This review comprehensively examines quantum-secure communication (QSC) frameworks for IoT-enabled CPS, focusing on Quantum Key Distribution (QKD), post-quantum cryptographic (PQC) algorithms, and hybrid quantum–classical security models suitable for constrained devices. A PRISMA-guided search of the Scopus and Google Scholar database was conducted in January 2026 using three keyword groups related to hybrid security, artificial intelligence, and cyber-physical systems. Based on the evaluation, 6008 publications have been identified between 2001 and 2026. The first-round screening was performed for 4948 articles, after excluding duplicates. During the screening stage, 348 articles were selected for abstract scrutiny, 115 records were excluded due to no direct focus on CPS/IoT applications, 52 studies were excluded because these papers relied on traditional security models, 25 studies were excluded due to insufficient relevance to the review objectives, and 15 additional non-English studies were removed. Following the screening stage, 141 studies were selected for full-text eligibility. Out of those, 86 studies were removed due to a lack of specific evaluation metrics or not being published in a peer-reviewed venue. Furthermore, the publications are classified as QKD-based secure CPS and QSC for industrial IoT, AI-Assisted Secure Communication for CPS Networks, and hybrid PQC-QKD models for CPS/IoT devices. This article investigates recent advancements in secure data transmission, verified protocols, and AI-driven anomaly detection customized to CPS/IoT environments. In addition, operational hurdles, interaction with open innovations, real-time deployment, and secure edge-cloud integration are highlighted. By analyzing recent developments and identifying research gaps, this review provides a structured roadmap for designing secure, scalable, and quantum-safe IoT-based CPS frameworks capable of withstanding next-generation cyber threats. This systematic review was performed and reported according to the PRISMA 2020 guidelines. Full article

This study analyzes cyberattack trends targeting non-profit organizations using longitudinal data collected over a three-year period within the Non-Profit Cybersecurity Incident Repository (NPCIR). Developed through a National Security Agency Center of Academic Excellence in Cyber Defense (NSA CAE-CD) designated center, the NPCIR applies an open-source intelligence (OSINT) methodology to systematically document cybersecurity incidents affecting the global non-profit sector. This study examines attack types, threat actor characteristics, sectoral distribution, and cybersecurity impacts using the Confidentiality–Integrity–Availability (CIA) triad framework. The results indicate that availability-related incidents, particularly ransomware and distributed denial-of-service (DDoS) attacks, constitute the most prevalent threats, while confidentiality breaches remain highly significant due to frequent data exposure incidents. Statistical analyses further demonstrate significant differences between non-profit organizations aligned with DHS CISA critical infrastructure sectors and those operating outside these sectors, especially regarding the prevalence of availability-focused attacks. In addition to its empirical contribution, the NPCIR initiative supports experiential learning opportunities for undergraduate and graduate students in cybersecurity and information technology. The resulting dataset provides actionable cyber threat intelligence for researchers, practitioners, and non-profit leaders seeking to strengthen organizational cybersecurity resilience and awareness. Full article