Search Results (809)

The Russia–Ukraine conflict has influenced global LNG shipping patterns; nevertheless, current research about its effects on the nodes and local regions of the LNG shipping network remains insufficient. This study employs a series of network metrics and a robustness evaluation model to examine the evolution in the structure and functionality of the LNG shipping network amid the Russia–Ukraine conflict, integrating LNG vessel origin–destination data from 2021 to 2023 to analyze the network’s structure and robustness. The research indicated that: (1) The alteration in trade relations instigated by the Russia–Ukraine conflict modified global LNG flows, resulting in a fragmented overall network structure and diminished transportation efficiency. The Russia–Ukraine conflict catalyzed the enhancement of European ports, leading to a substantial rise in the significance of premier European ports within the LNG transport network. Significant export ports, such as Ras Laffan, hold substantial importance within the network. (2) Among various assault techniques, degree-based intentional attacks inflict the greatest harm on the LNG shipping network. The robustness of the LNG shipping network declined following the Russia–Ukraine conflict, rendering it particularly susceptible in 2023. The findings indicate that the Russia–Ukraine conflict altered the structure of the LNG transportation network and diminished its robustness. The work holds substantial theoretical importance for examining the influence of geopolitical events on LNG transportation and for improving the maritime industry’s ability to navigate complicated circumstances. Full article

The development of medical data and resources has become essential for enhancing patient outcomes and operational efficiency in an age when digital innovation in healthcare is becoming more important. The rapid growth of the Internet of Medical Things (IoMT) is changing healthcare data management, but it also brings serious issues like data privacy, malicious attacks, and service quality. In this study, we present EdgeGuard, a novel decentralized architecture that combines blockchain technology, federated learning, and edge computing to address those challenges and coordinate medical resources across IoMT networks. EdgeGuard uses a privacy-preserving federated learning approach to keep sensitive medical data local and to promote collaborative model training, solving essential issues. To prevent data modification and unauthorized access, it uses a blockchain-based access control and integrity verification system. EdgeGuard uses edge computing to improve system scalability and efficiency by offloading computational tasks from IoMT devices with limited resources. We have made several technological advances, including a lightweight blockchain consensus mechanism designed for IoMT networks, an adaptive edge resource allocation method based on reinforcement learning, and a federated learning algorithm optimized for medical data with differential privacy. We also create an access control system based on smart contracts and a secure multi-party computing protocol for model updates. EdgeGuard outperforms existing solutions in terms of computational performance, data value, and privacy protection across a wide range of real-world medical datasets. This work enhances safe, effective, and privacy-preserving medical data management in IoMT ecosystems while maintaining outstanding standards for data security and resource efficiency, enabling large-scale collaborative learning in healthcare. Full article

Federated learning enables multiple intelligent devices to collaboratively perform machine learning tasks while preserving local data privacy. However, traditional FL architectures face challenges such as centralization and lack of effective defense mechanisms against malicious nodes, particularly in large-scale edge computing scenarios, which can lead to system instability. To address these challenges, this paper proposes a cross-zone secure federated learning method with blockchain and credibility mechanism, named CS-FL. By constructing a dual-layer blockchain network and introducing a blockchain ledger between zone servers, CS-FL establishes a decentralized trust mechanism for index detection and model aggregation. In node selection, CS-FL considers multiple dimensions, including node quality, communication resources, and historical credibility, and employs a three-stage mechanism that introduces lightweight probe tasks to assess node status before formal FL training, ensuring high-quality nodes participate. Additionally, the credibility incentive mechanism penalizes nodes that bypass probe mechanism and engage in malicious behaviors, effectively mitigating the impact of deceptive attacks. Experimental results show that CS-FL significantly improves the defense performance of FL, reducing attack success rates from 75–85% to below 5–20% when facing different types of threats, and effectively maintaining the training accuracy of the FL model. This demonstrates the potential of CS-FL to enhance the security and stability of FL systems in complex edge computing scenarios. Full article

The hindering of Global Navigation Satellite Systems (GNSS) signal reception by jamming and spoofing attacks degrades the signal quality. Careful attention needs to be paid when post-processing the signal under these circumstances before feeding the signal into the GNSS receiver’s post-processing stage. The identification of the time domain statistical attributes and the spectral domain characteristics play a vital role in analyzing the behaviour of the signal characteristics under various kinds of jamming attacks, spoofing attacks, and multipath scenarios. In this paper, the signal records of five disruptions (pure, continuous wave interference (CWI), multi-tone continuous wave interference (MCWI), multipath (MP), spoofing, pulse, and chirp) are examined, and the most influential features in both the time and frequency domains are identified with the help of explainable AI (XAI) models. Different Machine learning (ML) techniques were employed to assess the importance of the features to the model’s prediction. From the statistical analysis, it has been observed that the usage of the SHapley Additive exPlanations (SHAP) and local interpretable model-agnostic explanations (LIME) models in GNSS signals to test the types of disruption in unknown GNSS signals, using only the best-correlated and most important features in the training phase, provided a better classification accuracy in signal prediction compared to traditional feature selection methods. This XAI model reveals the black-box ML model’s output prediction and provides a clear explanation of the specific signal occurrences based on the individual feature contributions. By using this black-box revealer, we can easily analyze the behaviour of the GNSS ground-station signals and employ fault detection and resilience diagnosis in GNSS post-processing. Full article

Searchable encryption (SE) allows users to efficiently retrieve data from encrypted cloud data, but most of the existing SE solutions only support precise keyword search. Fuzzy searchable encryption agrees with practical situations well in the cloud environment, as search keywords that are misspelled to some extent can still generate search trapdoors that are as effective as correct keywords. In scenarios where multiple users can search for ciphertext, most fuzzy searchable encryption schemes ignore the security issues associated with malicious cloud services and are inflexible in multi-user scenarios. For example, in medical application scenarios where malicious cloud servers may exist, diverse types of files need to correspond to doctors in the corresponding departments, and there is a lack of fine-grained access control for sharing decryption keys for different types of files. In the application of medical cloud storage, malicious cloud servers may return incorrect ciphertext files. Since diverse types of files need to be guaranteed to be accessible by doctors in the corresponding departments, sharing decryption keys with the corresponding doctors for different types of files is an issue. To solve these problems, a verifiable fuzzy searchable encryption with blockchain-assisted multi-user scenarios is proposed. Locality-sensitive hashing and bloom filters are used to realize multi-keyword fuzzy search, and the bigram segmentation algorithm is optimized for keyword conversion to improve search accuracy. To realize fine-grained access control in multi-user scenarios, ciphertext-policy attribute-based encryption (CP-ABE) is used to distribute the shared keys. In response to the possibility of malicious servers tampering with or falsifying users’ search results, the scheme leverages the blockchain’s technical features of decentralization, non-tamperability, and traceability, and uses smart contracts as a trusted third party to carry out the search work, which not only prevents keyword-guessing attacks within the cloud server, but also solves the verification work of search results. The security analysis leads to the conclusion that the scheme is secure under the adaptively chosen-keyword attack. Full article

Gap dynamics are driving many important processes in the development of temperate forest ecosystems. What remains largely unknown is how often the regeneration processes initialized by endogenous mortality of dominant and co-dominant canopy trees take place. We conducted a study in the high mountain forests of the Central Western Carpathians, naturally dominated by the Norway spruce. Based on the repeated forest inventories in two localities, we quantified the structure and amount of deadwood, as well as the associated mortality of standing dead canopy trees. We determined the basic specific gravity of wood and anatomical changes in the initial phase of wood decomposition. The approach for estimating the rate of gap formation and the number of canopy trees per unit area needed for intentional gap formation was formulated based on residence time analysis of three localities. The initial phase of gap formation (standing dead tree in the first decay class) had a narrow range of residence values, with a 90–95% probability that gap age was less than 10 or 13 years. Correspondingly, a relatively constant absolute number of 12 and 13 canopy spruce trees per hectare died standing in 10 years, with a mean diameter reaching 50–58 cm. Maximum diameters trees (70–80 cm) were represented by 1–4 stems per hectare. The values of the wood-specific gravity of standing trees were around 0.370–0.380 g.cm⁻³, and varied from 0.302 to 0.523 g.cm⁻³. Microscopically, our results point out that gap formation is a continuous long-lasting process, starting while canopy trees are living. We observed early signs of wood degradation and bacteria, possibly associated with bark beetles, that induce a strong effect when attacking living trees with vigorous defenses. New information about the initial phase of gap formation has provided a basis for the objective proposal of intervals and intensities of interventions, designed to promote a diversified structure and the long-term ecological stability of the mountain spruce stands in changing climate conditions. Full article

The accuracy of node localization plays a crucial role in the performance and reliability of wireless sensor networks (WSNs), which are widely utilized in fields like security systems and environmental monitoring. The integrity of these networks is often threatened by the presence of malicious nodes that can disrupt the localization process, leading to erroneous positioning and degraded network functionality. To address this challenge, we propose the security-aware localization using bat-optimized malicious anchor prediction (BO-MAP) algorithm. This approach utilizes a refined bat optimization algorithm to improve both the precision of localization and the security of WSNs. By integrating advanced optimization with density-based clustering and probabilistic analysis, BO-MAP effectively identifies and isolates malicious nodes. Our comprehensive simulation results reveal that BO-MAP significantly surpasses six current state-of-the-art methods—namely, the Secure Localization Algorithm, Enhanced DV-Hop, Particle Swarm Optimization-Based Localization, Range-Free Localization, the Robust Localization Algorithm, and the Sequential Probability Ratio Test—across various performance metrics, including the true positive rate, false positive rate, localization accuracy, energy efficiency, and computational efficiency. Notably, BO-MAP achieves an impressive true positive rate of 95% and a false positive rate of 5%, with an area under the receiver operating characteristic curve of 0.98. Additionally, BO-MAP exhibits consistent reliability across different levels of attack severity and network conditions, highlighting its suitability for deployment in practical WSN environments. Full article

The massive usage of Internet services nowadays has led to a drastic increase in cyberattacks, including sophisticated techniques, so that Intrusion Detection Systems (IDSs) need to use AP technologies to enhance their effectiveness. However, this has resulted in a lack of interpretability and explainability from different applications that use AI predictions, making it hard to understand by cybersecurity operators why decisions were made. To address this, the concept of Explainable AI (XAI) has been introduced to make the AI’s decisions more understandable at both global and local levels. This not only boosts confidence in the AI but also aids in identifying different attributes commonly used in cyberattacks for the exploitation of flaws or vulnerabilities. This study proposes two developments: first, the creation and evaluation of machine learning models for an IDS with the objective to use Reinforcement Learning (RL) to classify malicious network traffic, and second, the development of a methodology to extract multi-level explanations from the RL model to identify, detect, and understand how different attributes affect uncertain types of attack categories. Full article

Correctly identifying influential nodes in a complex network and implementing targeted protection measures can significantly enhance the overall security of the network. Currently, indicators such as degree centrality, closeness centrality, betweenness centrality, H-index, and K-shell are commonly used to measure node influence. Although these indicators can identify critical nodes to some extent, they often consider node attributes from a narrow perspective and have certain limitations. Therefore, evaluating the importance of nodes using most existing indicators remains incomplete. In this paper, we propose the multi-attribute CRITIC-TOPSIS network decision indicator, or MCTNDI, which integrates closeness centrality, betweenness centrality, H-index, and network constraint coefficients to identify critical nodes in a network. This indicator combines information from multiple perspectives, including local neighborhood importance, network topological location, path centrality, and node mutual information, thereby solving the issue of the one-sided perspective of single indicators and providing a more comprehensive measure of node importance. Additionally, MCTNDI is validated through the analysis of several real-world networks, including the Contiguous USA network, Dolphins network, USAir97 network, and Tech-routers-rf network. The validation is conducted from four aspects: the results of simulated network attacks, the distribution of node importance, the monotonicity of rankings, and the similarity of indicators, illustrating MCTNDI’s effectiveness in real networks. Full article

The increase in set-valued data such as transaction records and medical histories has introduced new challenges in data anonymization. Traditional anonymization techniques targeting structured microdata comprising single-attribute- rather than set-valued records are often insufficient to ensure privacy protection in complex datasets, particularly when re-identification attacks leverage partial background knowledge. To address these limitations, this study proposed the Local Generalization and Reallocation (LGR) + algorithm to replace the Normalized Certainty Penalty loss measure (hereafter, NCP) used in traditional LGR algorithms with the Information Gain Heuristic metric (hereafter, IGH). IGH, an entropy-based metric, evaluates information loss based on uncertainty and provides users with the advantage of balancing privacy protection and data utility. For instance, when IGH causes greater information-scale data annotation loss than NCP, it ensures stronger privacy protection for datasets that contain sensitive or high-risk information. Conversely, when IGH induces less information loss, it provides better data utility for less sensitive or low-risk datasets. The experimental results based on using the BMS-WebView-2 and BMS-POS datasets showed that the IGH-based LGR + algorithm caused up to 100 times greater information loss than NCP, indicating significantly improved privacy protection. Although the opposite case also exists, the use of IGH introduces the issue of increased computational complexity. Future research will focus on optimizing efficiency through parallel processing and sampling techniques. Ultimately, LGR+ provides the only viable solution for improving the balance between data utility and privacy protection, particularly in scenarios that prioritize strong privacy or utility guarantees. Full article

Symmetry-breaking in security mechanisms can create vulnerabilities which attackers may exploit to gain unauthorized access or cause data leakage, ultimately compromising the integrity and security of vector geographic data. How to achieve tamper localization remains a challenging task in the field of data authentication research. We propose a precise authentication watermarking algorithm for vector geographic data based on multiple sorting mechanisms. During the watermark embedding process, a sequence of points is initially extracted from the original data, followed by embedding watermarks into each coordinate point. The embedded watermark information consists of the self-identification and ordering information of each coordinate point. Ordering information is crucial for establishing relationships among points and enhancing tamper localization. During the authentication phase, the extracted watermark information is compared with the newly generated watermark information. Self-identification information is used to authenticate addition attacks, while ordering information is used to authenticate deletion attacks. Experimental results demonstrate that the proposed algorithm achieves high precision in detecting and localizing both addition and deletion attacks, significantly outperforming the comparison method. Full article

The increasing threat of Distributed DDoS attacks necessitates robust, big data-driven methods to detect and mitigate complex Network and Transport Layer (NTL) attacks. This paper proposes EffiGRU-GhostNet, a deep-learning ensemble model for high-accuracy DDoS detection with minimal resource consumption. EffiGRU-GhostNet integrates Gated Recurrent Units (GRU) with the GhostNet architecture, optimized through Principal Component Analysis with Locality Preserving Projections (PCA-LLP) to handle large-scale data effectively. Our ensemble was tested on IoT-23, APA-DDoS, and additional datasets created from popular DDoS attack tools. Simulations demonstrate a recognition rate of 98.99% on IoT-23 with a 0.11% false positive rate and 99.05% accuracy with a 0.01% error on APA-DDoS, outperforming SVM, ANN-GWO, GRU-RNN, CNN, LSTM, and DBN baselines. Statistical validation through Wilcoxon and Spearman’s tests further verifies EffiGRU-GhostNet’s effectiveness across datasets, with a Wilcoxon F-statistic of 7.632 (p = 0.022) and a Spearman correlation of 0.822 (p = 0.005). This study demonstrates that EffiGRU-GhostNet is a reliable, scalable solution for dynamic DDoS detection, advancing the field of big data-driven cybersecurity. Full article

Background/Hypothesis: Motion sensitivity symptoms, such as dizziness or unsteadiness, are frequently reported as non-headache symptoms of migraine. Postural imbalance has been observed in subjects with vestibular migraine, chronic migraine, and aura. We aimed to assess the ability of largest Lyapunov’s exponent for a short time series (sLLE), which reflects the ability to cope with internal perturbations during gait, to detect differences in local dynamic stability between individuals with migraine without aura (MO) with an episodic pattern between attacks and healthy subjects (HS). Methods: Trunk accelerations of 47 MO and 38 HS were recorded during gait using an inertial measurement unit. The discriminative ability of sLLE was assessed through receiver-operating characteristics curves and cutoff analysis. Partial correlation analysis was conducted between the clinical and gait variables, excluding the effects of gait speed. Results: MO showed higher sLLE values, and reduced pelvic rotation, pelvic tilt, and stride length values. sLLEML and pelvic rotation showed good ability to discriminate between MO and HS and were correlated with the perceived pain, migraine disability assessment score, and each other. Conclusions: these findings may provide new insights into the postural balance control mechanism in subjects with MO and introduce the sLLEML as a potential measure of dynamic instability in MO. Full article

Background/Objectives: Multiple dehiscences of the otic capsule can exhibit behavior similar to Ménière’s disease, not only from a clinical perspective but also in the results of audiovestibular tests. The main objective of this study is to characterize third mobile window etiologies from an audiovestibular perspective, while also evaluating the therapeutic response to four different treatment protocols. Furthermore, we aim to explore a potential association with the development of radiologically defined endolymphatic hydrops (EH). Methods: This is a retrospective cohort study conducted from 2017 to 2024 at a tertiary-level otology and otoneurology unit. All patients underwent pure tone audiometry, vHIT, cVEMP, and oVEMP. Some of these patients, selected under rigorous inclusion criteria based on clinical and audiometric findings, were subjected to a 4-h delayed intravenous gadolinium-enhanced 3D-FLAIR MRI. Results: We obtained a sample of 86 patients, with a mean age of 52.2 ± 7.64 years: 62.76% were female (n = 54) and 37.21% were male (n = 32); 88.37% (n = 76) were diagnosed with superior semicircular canal dehiscence syndrome (SSCDS), while 11.62% (n = 10) had other forms of otic capsule dehiscence. The most common symptom observed was unsteadiness (44%). While surgery is the only curative treatment, other medical treatments, such as acetazolamide, also helped reduce symptoms such as autophony, falls, instability, and vertigo attacks, with a relative risk reduction (RRR) exceeding 75% (95% CI, p < 0.05). The results of the MRI in EH sequences indicate that 7.89% of the patients diagnosed with SSCDS also developed radiological EH, compared to 40.00% of the patients with other otic capsule dehiscences, a difference that was statistically significant (p = 0.0029. Conclusions: Otic capsule dehiscences are relatively unknown conditions that require clinical diagnosis. Although VEMP testing is useful, imaging studies are necessary to localize and characterize the defect, most commonly found in the superior semicircular canal. We should consider these dehiscences in cases where there is a suspicion of EH development. Further research, including in vivo neuroimaging studies using hydrops sequences, is required to better understand their relationship to potential Ménière’s disease. Full article

Continuous-variable (CV) measurement-device-independent (MDI) quantum key distribution (QKD) can remove the feasible side-channel attacks on detectors based on the accurate Bell-state measurement (BSM), where an optical amplitude modulator (AM) plays a crucial role in managing the intensity of the transmitted light pulse. However, the AM-involved practical security has remained elusive as the operating frequency of the AM usually determines the actual secret key rate of the CV-MDI-QKD system. We find that an imperfect pulse generated from the AM at high speed can lead to a challenge to the practical security as a minor intensity change of the light pulse can bring about a potential information leakage. Taking advantage of this flaw, we suggest an attack strategy targeting the embedded AM in CV-MDI-QKD without sending the local oscillator (LO). This attack can damage the AM and thus decrease the estimated secret key rate of the system even when the orthogonal local LO (LLO) scheme is carried out. To assess the practical security risk resulting from the leaked information from the AM, we conduct numerical simulations to demonstrate the influence of the AM on the CVMDI-QKD system. Full article