Search Results (286)

The secure management of classified documents containing sensitive information is critical for governments, military organizations, and the industry. Traditional data loss prevention (DLP) systems lack robustness against insider threats, particularly regarding access log integrity and tamper-proof auditing. To address log security, the previous literature has proposed multiple solutions, including private and hybrid blockchain models (e.g., Ethereum + MultiChain) to ensure audit trail integrity. However, hybrid architectures often face challenges such as unpredictable transaction costs (gas fees) and potential privacy risks when scaled for enterprise DLP logs. Conversely, private architectures may require higher resources, potentially causing bottlenecks on endpoints. In this paper, we propose an optimized Blockchain-Based Classified Document Management System (B2CDMS) utilizing a permissioned architecture. Our work demonstrates the challenges, advantages, and weak points of current solutions. We optimized a permissioned blockchain (BC) (Hyperledger Fabric v2.5) with an External Chaincode Builder using the Chaincode-as-a-Service (CCaaS) pattern. We compared our proposed private architecture with a hybrid architecture (Ethereum + MultiChain) and a public solution (Ethereum). We conducted a comprehensive analysis using pseudo Trellix ePolicy Orchestrator (ePO) Data Loss Prevention (DLP) logs. Experimental results on an Apple Silicon M4 (Apple Inc., Cupertino, CA, USA) testbed show that the proposed architecture achieves a throughput of 845.8 Transactions Per Second (TPS) with a sub-second latency of 55 ms, aiming to eliminate the bottlenecks of public blockchains. Furthermore, the system introduces a privacy-preserving hashing mechanism (i.e., committing only deterministic Secure Hash Algorithm 256-bit (SHA-256) digests to the immutable ledger while keeping the actual sensitive Personally Identifiable Information (PII) strictly in off-chain databases) compliant with General Data Protection Regulation (GDPR). It ensures that classified document metadata remains immutable and secure against rogue access benefiting from admin privileges. This study concludes that permissioned blockchain architectures offer a scalable and resource-efficient solution for forensic evidence preservation throughout the classified document lifecycle. Full article

Both plants and animals have developed a sophisticated two-tiered innate immune system. This involves an initial recognition of microbial patterns conserved on the cell surface (PAMP-triggered immunity) and a subsequent more specific intracellular recognition of pathogenic effectors or their activities (effector-triggered immunity). A common fundamental feature is the use of NLR-like intracellular receptors to detect insider threats. Both plant NLRs (receptors containing nucleotide-binding domains and leucine-rich repeats) and animal NLRs (NOD-like receptors) share a modular tripartite architecture, typically featuring a central nucleotide-binding domain (NBD/NOD) and C-terminal leucine-rich repeats (LRRs). The NBD/NOD is crucial for facilitating the exchange of ADP/ATP, acting as a molecular switch to promote oligomerization and activation of NLRs in both kingdoms. In this review, we summarize the similarities and differences between plant and animal molecular perception and immunity mechanisms. Additionally, we highlight the fact that some human pathogens can infect plants, and crucially, some plant pathogens are capable of causing disease in humans. This suggests conserved molecular strategies to invade and manipulate host cells belonging to different biological kingdoms, uncovering that plant and human pathology may benefit from future investigations in their respective fields. Full article

Mobile data collectors (MDCs) play a very important role in Internet of Things (IoT) sensing networks. However, ensuring their trustworthiness against insider threats, such as on–off attacks and spatiotemporal fabrication, remains a critical challenge. Existing trust evaluation methods frequently struggle with these threats due to insufficient evidence dimensions and the inability to quantify behavioral stability. To address these limitations, this paper proposes an enhanced proactive trust evaluation system based on stability sequence extraction (E-PTES-S). E-PTES-S improves the evaluation accuracy by integrating five factors of evidence, stability-computation mechanisms, and an adaptive weight allocation scheme to maintain robustness even when proactive verification data is scarce. In addition to the usual interaction and proactive verification indicators, regional consistency (${\mathcal{T}}_{RC}$) and task timeliness (${\mathcal{T}}_{TT}$) are introduced to mitigate location falsification and transmit-time deviations more rigorously. Then, a sliding window technique is used to obtain an integrated evidence sequence, which includes a new continuous stability sequence (${\mathcal{F}}_{CSS}$) and traditional credible, untrustworthy, and uncertain sequences. This continuous stability sequence adds a variance-based incentive scheme to measure behavioral stability. Finally, the normalized trust value is derived from multiple indicators including multidimensional spatiotemporal evidence and stability metrics. Experimental results show that the proposed E-PTES-S achieves a normal node detection rate of 98.7% under complex dynamic conditions, outperforming the baseline PTES and Trust-SIoT algorithms by approximately 9% and 1%, respectively, while also improving the cumulative data collection profit by 4.8%. Furthermore, robustness analysis demonstrates that E-PTES-S exhibits excellent robustness against physical-layer uncertainties, successfully sustaining an 84.4% detection rate even under severe environmental shadowing. Full article

Cross-kingdom pathogenesis—human and animal pathogens colonizing and persisting in plants—is transforming our understanding of microbial ecology, food safety, and public health. This review translates incoming research that demonstrates plants as more than mute carriers to dynamic ecological interfaces where human and zoonotic pathogens, such as Salmonella enterica, Escherichia coli O157:H7, and Listeria monocytogenes, will adhere, internalize, and, in some cases, potentially evade host defenses. Such pathogens exploit evolutionarily conserved molecular processes like Type III secretion system 1 (TTSS), biofilm formation, quorum sensing, and small RNA-mediated immune sabotage that have allowed them to cross biological kingdom boundaries. To provide an entry point for pathogens, environmental conditions (e.g., contaminated irrigation water, manure application, wildlife access, and mechanical wounding) promote pathogen transfer to and penetration into plant tissues through stomata hydathodes above ground or roots below ground. Once inside, pathogens confront a range of plant immune responses, indigenous microbiota, and abiotic stresses such as UV radiation exposure, nutrient starvation, and osmotic fluctuations. Nonetheless, biofilm production, metabolic versatility, and virulence gene expression contribute to their persistence. Interactions with plant pathogens and microbiomes additionally shape colonization dynamics, for example, through co-survival and niche manipulation. With the acceleration of these processes due to climate change, urbanization, and intensified agriculture, cross-kingdom pathogenesis becomes a rising concern for One Health. Critical knowledge gaps, including seedborne transmission, microbiome engineering, and predictive modeling, are pointed out in the review along with emerging mitigation strategies, including point-of-care diagnostics and microbial biocontrol. In conclusion, this review advocates for interdisciplinary collaboration from microbiology, plant science, and One Health perspectives to predict and mitigate cross-kingdom threats to global food production. Full article

When two floating bodies are engaged in side-by-side operations, gap resonance is prone to occur. This phenomenon leads to violent, large-amplitude fluid motions inside the gap, posing a serious threat to operational safety. To address this issue, the present study establishes a numerical wave tank based on a two-way coupled potential–viscous flow method. In the vicinity of the floating bodies, viscous flow is solved to capture nonlinear effects; in the far field, a potential flow solver is employed to simulate wave propagation. Information exchange between the two domains is achieved through a two-way coupling strategy involving coupling interfaces and relaxation zones. Then, the numerical method is validated by simulating the gap wave elevation and the sway motion of a floating body under regular waves, with computed results compared against experimental data. Subsequently, to reveal the distinct roles of fixed and moving bodies in modulating gap resonance behavior, the hydrodynamic interactions between two identical floating bodies in regular waves are investigated under two representative configurations, one in which both bodies remain fully fixed, and another in which the upstream body is held fixed while the downstream body is allowed coupled motion in three degrees of freedom. The results demonstrate that the multi-degree-of-freedom (DOF) motion of the downstream floating body has a significant effect on the behavior of the resonance frequency and amplitude of the gap resonance. Full article

Underwater Acoustic Sensor Networks (UASNs) are pivotal for environmental monitoring, surveillance, and marine data collection. However, their open and largely unattended operational settings, constrained communication capabilities, limited energy resources, and susceptibility to insider attacks make it difficult to achieve safe, secure, and efficient collaborative learning. Federated learning (FL) offers a privacy-preserving method for decentralized model training but is inherently vulnerable to Byzantine threats and malicious participants. This paper proposes trust-aware FL for underwater sensor networks (TAFL-UWSN), a trust-aware FL framework designed to improve security, reliability, and energy efficiency in UASNs by incorporating trust evaluation directly into the FL process. The goal is to mitigate the impact of adversarial nodes while maintaining model performance in low-resource underwater environments. TAFL-UWSN integrates continuous trust scoring based on packet forwarding reliability, sensing consistency, and model deviation. Trust scores are used to weight or filter model updates both at the node level and the edge layer, where Autonomous Underwater Vehicles (AUVs) act as mobile aggregators. A trust-aware federated averaging algorithm is implemented, and extensive simulations are conducted in a custom Python-based environment, comparing TAFL-UWSN to standard FedAvg and Byzantine-resilient FL approaches under various attack conditions. TAFL-UWSN achieved a model accuracy exceeding 92% with up to 30% malicious nodes while maintaining a false positive rate below 5.5%. Communication overhead was reduced by 28%, and energy usage per node dropped by 33% compared to baseline methods. The TAFL-UWSN framework demonstrates that integrating trust into FL enables secure, efficient, and resilient underwater intelligence, validating its potential for broader application in distributed, resource-constrained environments. Full article

To break the long-standing imbalance of “easy to attack, hard to defend” in cyberspace, cyberspace mimic defense (CMD) has been proposed. It provides active defense against known and unknown vulnerabilities and backdoors via a dynamic heterogeneous redundancy (DHR) mechanism. Although the DHR architecture can suppress disturbances from physical factors or endogenous faults, data processing and transmission inside the architecture are usually in plaintext, which poses severe threats to data privacy. To address this problem, we propose a mimic active defense method based on multiple encryption structure encodings. For heterogeneity, an encryption/decryption component set module is designed to achieve data-level heterogeneity among executors. For dynamics, an executor identifier encryption scheme based on the elliptic curve digital signature algorithm (ECDSA) is used to protect the executor selection process. Meanwhile, a dynamic scheduling algorithm based on historical confidence is applied to reduce the impact of faulty executors. Experimental results show that the proposed method has obvious advantages in data privacy protection in terms of average historical confidence, execution efficiency, and bit error rate. Full article

Unmanned aerial vehicle (UAV) applications are growing fast in different sectors, such as agricultural, commercial, academic, leisure, and health fields. However, drones pose a significant threat to public safety due to their ability to transmit information, particularly when used in an unauthorized or malicious manner. In fact, in order to protect citizens’ privacy and prevent accidents in high-traffic areas due to poorly controlled flights, no-fly zones for drones have been established in the legislation of a number of countries. Most common UAV detection techniques are based on radio frequencies, which identify drones and their models by monitoring radio frequency signals. However, differentiating between multiple UAVs of the same model is their main limitation. This article fills this gap by proposing a method for physically tracking the communication frames of a registered UAV in the presence of another UAV of the same model. A measurement campaign was conducted to collect real-world RF communication signals from two DJI MAVIC 2 Zoom, two DJI Air2S, and two DJI Phantom drones. This measurement was performed inside and outside an anechoic chamber in order to study the UAV’s communication without any interference and in the presence of other communications. Through detailed statistical analysis, we characterized features such as communication duration, time intervals between communications, signal strength, and patterns in communication timing sequences. Our analysis revealed unique, identifiable patterns for each UAV, even within identical models. Based on these results, we developed an automated system that links communication frames to the corresponding registered drones. The proposed method fills gaps in drone detection and surveillance models, providing valuable information for applications in the fields of security and airspace management. This research lays the foundation for drone identification solutions, thereby addressing a major limitation of current detection technologies. Full article

5G systems have delivered on their promise of seamless connectivity and efficiency improvements since their global rollout began in 2020. However, maintaining subscriber identity privacy on the network remains a critical challenge. The 3GPP specifications define numerous identifiers associated with the subscriber and their activity, all of which are critical to the operations of cellular networks. While the introduction of the Subscription Concealed Identifier (SUCI) protects users across the air interface, the 5G Core Network (CN) continues to operate largely on the basis of the Subscription Permanent Identifier (SUPI)—the 5G-equivalent to the IMSI from prior generations—for functions such as authentication, billing, session management, emergency services, and lawful interception. Furthermore, the SUPI relies solely on the transport layer’s encryption for protection from malicious observation and tracking of the SUPI across activities. The crucial role of the largely unprotected SUPI and other closely related identifiers creates a high-value target for insider threats, malware campaigns, and data exfiltration, effectively rendering the Mobile Network Operator (MNO) a single point of failure for identity privacy. In this paper, we analyze the architectural vulnerabilities of identity persistence within the CN, challenging the legacy “honest-but-curious” trust model. To quantify the extent of subscriber identities being utilized and exchange within various API calls in the CN, we conducted a study of the occurrence of SUPI as a parameter throughout the collection of 5G SBI (Service-Based Interface) Core VNF (Virtual Network Function) API (Application Programming Interface) schemas. Our extensive analysis of the 3GPP specifications for 3GPP Release 18 revealed a total of 4284 distinct parameter names being used across all API calls, with a total of 171,466 occurrences across the API schema. More importantly, it revealed a highly skewed distribution in which subscriber identity plays a pivotal role. Specifically, the “supi” parameter ranks 57th with 397 occurrences. We found that SUPI occurs both as a direct parameter (“supi”) and within 72 other parameter names that contain subscriber identifiers as defined in 3GPP TS 23.003. For these 73 parameter names, we identified a total of 8757 occurrences. At over 5.11% of all parameter occurrences, this constitutes a disproportionately large share of total references. We also detail scenarios where subscriber privacy can be compromised by internal actors and review future privacy-preserving frameworks that aim to decouple subscriber identity from network operations. By suggesting a shift towards a zero-trust model for CN architecture and providing subscribers with greater control over their identity management, this work also offers a potential roadmap for mitigating insider threats in current deployments and influencing specific standardization and regulatory requirements for future 6G and Beyond-6G networks. Full article

Intentional high-power electromagnetic (EM) interference poses a serious threat to sensitive electronic systems and often manifests as ultra-wideband (UWB) sub- and nanosecond pulses. Metallic shielding enclosures with technological apertures are commonly used for protection; however, apertures enable electromagnetic coupling into the enclosure and limit shielding performance. While most existing studies focus on transient disturbances with durations exceeding the enclosure transit time, this work addresses an ultrashort high-power subnanosecond UWB plane-wave pulse whose duration is significantly shorter than the enclosure transit time, a regime that remains insufficiently explored. A time-domain numerical analysis is performed for a low-profile rectangular metallic enclosure with a front-wall aperture, focusing on internal EM field evolution, internal pulse formation, and polarization-dependent shielding effectiveness. Three-dimensional full-wave simulations were carried out using CST Microwave Studio over a 90 ns observation window. The results show that the incident pulse excites primary subnanosecond EM waves inside the enclosure, which subsequently generate secondary waves through multiple reflections from the enclosure walls. Their interaction produces complex, long-lasting, time-varying internal field patterns. Although attenuated, the resulting internal subnanosecond pulses repeatedly traverse the enclosure interior, forming a pulse train-like sequence that may pose a cumulative electromagnetic threat to internal electronics. A key contribution of this work is the quantification of time-dependent local shielding effectiveness for both electric and magnetic fields, derived directly from the internal pulse train-like series obtained in the time domain. The concept of local, time-dependent shielding effectiveness provides physical insight that cannot be obtained from a single globally averaged SE value. In the case of ultrashort electromagnetic pulse excitation, the internal field response of an enclosure is strongly non-stationary and highly non-uniform in space, with local field maxima occurring at specific times and locations despite good average shielding performance. Time-dependent local SE enables identification of worst-case temporal conditions, repeated high-amplitude internal exposures, and critical regions inside the enclosure where shielding is significantly weaker than suggested by global metrics. Therefore, while conventional SE remains useful as a summary measurand, local time-dependent SE is essential for assessing the actual electromagnetic risk to sensitive electronics under ultrashort pulse disturbances. In addition, a global shielding effectiveness metric mapped over selected enclosure cross-sections is introduced to enable rapid visual assessment of shielding performance. The analysis demonstrates a strong dependence of internal wave propagation, internal pulse formation, and both local and global shielding effectiveness on the polarization of the incident subnanosecond EM pulse. These findings provide new physical insight into aperture coupling and shielding behavior in the ultrashort-pulse regime and offer practical guidance for the assessment and design of compact shielding enclosures exposed to high-power UWB EM threats. Full article

Distributed databases are increasingly used in enterprise and cloud environments, but their distributed architecture introduces significant security challenges, including data leaks and insider threats. In the context of escalating cyber threats targeting large-scale distributed databases and cloud-native microservice architectures, this paper presents Graph Metric Dimension-based Anomaly Detection (GMD-AD), a novel graph-structure model designed to enhance cybersecurity in distributed databases by leveraging the metric dimension of interaction graphs; further, GMD-AD addresses the critical need for real-time, low-overhead, and privacy-aware anomaly detection mechanisms. The model introduces a compact resolving set as landmarks to detect intrusions through distance vector variations with minimal computational overhead. The proposed framework offers four major contributions, including sequential metric dimension updates to support dynamic topologies; a parallel BFS strategy to enable scalable processing; the incorporation of the k-metric anti-dimension to provide provable privacy against re-identification attacks; and a hybrid pipeline in which resolving-set subgraphs are processed by graph neural networks prior to final classification using gradient boosting. Experiments conducted on the SockShop microservices benchmark and a real MongoDB sharded cluster with injected anomalies reveal 60% reduced localization latency (1200 ms → 480 ms), stable detection accuracy (>0.997), increased noise robustness (F1 0.95 → 0.97) and a drop of re-identification success rate from the baseline by 40 percentage points (68% → 28%) when k = 3, ℓ = 2. We demonstrated up to 60% latency reduction and 40% privacy improvement over baselines, validated on real MongoDB clusters. The findings show that GMD-AD is a scalable, real-time and privacy-preserving HTTP anomaly detection solution for both distributed database systems and microservice architectures. Full article

With the rise of smart cities, technology has enabled more efficient urban management. A key part of this is the Internet of Vehicles (IoVs), which connects vehicles to smart city systems to improve transportation safety and efficiency. This integrated system enables wireless connection between vehicles, allowing for the sharing of essential traffic information. However, with all this connectivity, there are growing concerns about IoV security and privacy. This paper presents a new privacy-preserving authentication scheme for Autonomous Vehicles (AVs) in the IoV field using physical unclonable functions (PUFs). This scheme employs a bilinear pairing-based encryption technique that supports search over encrypted data. The primary aim of this scheme is to authenticate AVs inside the IoV architecture. A novel PUF design generates random keys for our authentication technique, hence boosting security. This dual-layer security strategy safeguards against a range of cyber threats, including identity fraud, man-in-the-middle attacks, and unauthorized access to personal user data. The PUF design will guarantee the true randomness of the AVs’ users’ secret keys. To handle the large amount of data involved, we use hardware acceleration with different Field-Programmable Gate Arrays (FPGAs). Our examination of privacy and security demonstrates the achievement of the defined design goals. The proposed authentication framework was fully implemented and validated on FPGA platforms to demonstrate its hardware feasibility and efficiency. The integrated heterogeneous PUF achieves an average reliability exceeding $98.5\%$ across a wide temperature range, while maintaining near-ideal randomness with an average Hamming weight of $49.7\%$ over multiple challenge sets. Furthermore, the uniqueness metric approaches $49.9\%$, confirming strong inter-device distinguishability among different PUF instances. The complete authentication architecture was synthesized on Nexys-100T, Zynq-104, and Kintex-116 devices, where the design utilizes less than $80\%$ of slice Look-Up Tables (LUTs), under $27\%$ of on-chip memory resources, and below $16\%$ of DSP blocks, demonstrating low hardware overhead. Full article

The rapid digital transformation of healthcare has improved clinical efficiency, patient engagement, and data accessibility, but it has also introduced significant cyber security and data privacy challenges. Healthcare IT systems increasingly rely on interconnected networks, electronic health records (EHRs), tele-medicine platforms, cloud infrastructures, and Internet of Medical Things (IoMT) devices, which collectively expand the attack surface for cyber threats. This scoping review maps and synthesizes recent evidence on cyber security risks in healthcare, including ransomware, data breaches, insider threats, and vulnerabilities in legacy systems, and examines key data privacy concerns related to patient confidentiality, regulatory compliance, and secure data governance. We also review contemporary security strategies, including encryption, multi-factor authentication, zero-trust architecture, blockchain-based approaches, AI-enabled threat detection, and compliance frameworks such as HIPAA and GDPR. Persistent challenges include integrating robust security with clinical usability, protecting resource-limited hospital environments, and managing human factors such as staff awareness and policy adherence. Overall, the findings suggest that effective healthcare cyber security requires a multi-layered defense combining technical controls, continuous monitoring, governance and regulatory alignment, and sustained organizational commitment to security culture. Future research should prioritize adaptive security models, improved standardization, and privacy-preserving analytics to protect patient data in increasingly complex healthcare ecosystems. Full article

As digital technology becomes increasingly integral to modern industries, the risks posed by cyber threats, including malware, ransomware, and insider attacks, continue to rise, jeopardizing critical infrastructure including renewable energy system. The world is more vulnerable to sophisticated cyberattacks due to its reliance on smart grids and IoT-enabled renewable energy systems. Without specialized digital forensic frameworks, incident response and critical infrastructure resilience are limited. This research examines the pivotal role of digital forensics in defending renewable energy system against the growing wave of cyber threats. The study highlights the significance of digital forensics in enhancing incident response, evidence collection, and forensic analysis capabilities. Through detailed case studies, it investigates the implementation strategies of digital forensics to identify, track, and mitigate cyber risks. To address this objective, this study proposes a comprehensive and adaptive cybersecurity framework that integrates digital forensics and fuzzy multi-criteria decision-making to enhance cyber resilience in renewable energy systems. Drawing on relevant case studies, the research demonstrates how the integration of digital forensics with fuzzy logic supports dynamic threat evaluation and risk mitigation. Comparative analysis show that the proposed framework outperforms traditional methods in terms of detection accuracy, response time, and adaptability to evolving threat landscapes. Key contributions include: (1) a structured digital forensics-based cybersecurity model tailored to renewable energy systems, (2) application of fuzzy Analytical Hierarchy Process (AHP) for multi-criteria threat evaluation, and (3) policy-oriented recommendations for stakeholders to reinforce national cyber resilience in line with energy transition. The findings underscore the need for a cohesive cybersecurity strategy grounded in advanced decision-support systems to protect the future of sustainable energy. Full article

The issue of overpressure at fire doors in escape passage is often overlooked in traditional tunnel design. Current design approaches tend to overemphasize maintaining positive pressure inside the passage for smoke prevention, which results in excessive resistance when opening fire doors. This can hinder emergency evacuation efficiency and pose a threat to personnel safety. This study focused on a typical 1000-m-long straight escape passage to investigate the overpressure problem of fire doors in highway tunnels from both theoretical and empirical perspectives. Traditional pressure calculations for tunnel escape passages adopt relevant guiding designs from the building category, which may lead to certain errors. Therefore, on this basis, this paper employs pressure calculation equations based on the specific pipeline characteristics of smoke control systems. By solving the pressure calculation equations for the fire doors in escape passages, the thrust required to open the doors in the closed state was analyzed. Results show that the force needed to open a fire door can reach up to 168 N under fire conditions, which far exceeds the allowable limits stipulated in relevant design standards. Furthermore, the results indicate that the maximum allowable length of the escape passage should not exceed 3200 m within acceptable pressure limits through numerical simulation. A mathematical relationship between passage length and fire door pressure was also established, confirming the accuracy of the maximum allowable passage length. This study analyzed the hazards of overpressure in escape passages and proposes a method for determining the maximum permissible passage length, aiming to balance the requirements of smoke control with the safety of personnel evacuation. Full article