Search Results (40,440)

The growing sophistication of cyber threats necessitates robust and interpretable intrusion detection systems (IDS) to safeguard network security. While machine learning models such as Decision Tree (DT), Random Forest (RF), k-Nearest Neighbors (K-NN), and XGBoost demonstrate high effectiveness in detecting malicious activities, their interpretability decreases as their complexity and accuracy increase, posing challenges for critical cybersecurity applications. Local Interpretable Model-agnostic Explanations (LIME) is widely used to address this limitation; however, its reliance on normal distribution for perturbations often fails to capture the non-linear and imbalanced characteristics of datasets like CIC-IDS-2018. To address these challenges, we propose a modified LIME perturbation strategy using Weibull, Gamma, Beta, and Pareto distributions to better capture the characteristics of network traffic data. Our methodology improves the stability of different ML models trained on CIC-IDS datasets, enabling more meaningful and reliable explanations of model predictions. The proposed modifications allow for an increase in explanation fidelity by up to 78% compared to the default Gaussian approach. Pareto-based perturbations provide the best results. Among all distributions tested, Pareto consistently yielded the highest explanation fidelity and stability, particularly for K-NN ( = 0.9971, S = 0.9907) and DT ( = 0.9267, S = 0.9797). This indicates that heavy-tailed distributions fit well with real-world network traffic patterns, reducing the variance in attribute importance explanations and making them more robust. Full article

In modern agriculture, timely and accurate crop yield information is crucial for optimising agricultural production management and resource allocation. This study focused on improving the prediction accuracy of pear yields. Taking Alar City, Xinjiang, China as the research area, a variety of data including leaf area index (LAI), soil moisture (SM) and remote sensing data were collected, covering four key periods of pear growth. Three advanced algorithms, Partial Least Squares Regression (PLSR), Support Vector Regression (SVR) and Random Forest (RF), were used to construct the regression models of LAI and vegetation index in four key periods using Sentinel-2 satellite remote sensing data. The results showed that the RF algorithm provided the best results when inverting the LAI. The coefficients of determination (R²) were 0.73, 0.72, 0.76, and 0.77 for the four periods, respectively, and the root-mean-square errors (RMSE) were 0.21 m²/m², 0.24 m²/m², 0.18 m²/m², and 0.16 m²/m², respectively. Therefore, the RF algorithm was selected as the preferred method for LAI inversion in this study. Subsequently, the study further explored the potential of data assimilation techniques in enhancing the accuracy of pear yield simulation. LAI and SM were incorporated into the World Food Studies (WOFOST) crop growth model by four assimilation algorithms, namely, the Four-Dimensional Variational Approach (4D-Var), Particle Swarm Optimisation (PSO) algorithm, Ensemble Kalman Filter (EnKF), and Particle Filter (PF) in separate and joint assimilation, respectively. The experimental results showed that the assimilated model significantly improved the accuracy of yield prediction compared to the unassimilated model. In particular, the EnKF algorithm provided the highest accuracy in yield estimation with R² of 0.82, 0.79 and RMSE of 1056 kg/ha and 1385 kg/ha when LAI alone and SM alone were assimilated, whereas 4D-Var performed the best when LAI and SM were jointly assimilated, with R² as high as 0.88, and the RMSE reduced to 923 kg/ha. In addition, it was found that assimilating LAI outperformed assimilating SM when assimilating one variable, whereas joint assimilation of LAI and SM further enhanced the predictive performance beyond that of assimilating one variable alone. In summary, the present study demonstrated great potential to provide strong support for accurate prediction of pear yield by effectively integrating LAI and SM into crop growth models through data assimilation. Full article

In this paper, we introduce two novel top-k spatial dataset search schemes, KSDS and KSDS+. The core innovation of these schemes lies in partitioning the spatial datasets into grids and assessing similarity based on the distribution of points within these grids. This approach provides a robust foundation for spatial dataset search. To optimize search performance, we have developed an optimized scheme that incorporates two key strategies: a GMBR-based optimization strategy and a pooling-based optimization strategy. These strategies are designed to filter datasets to significantly improve search efficiency. Our experimental results demonstrate that KSDS and KSDS+ can perform top-k spatial dataset searches with both high effectiveness and efficiency, outpacing existing methods in terms of search speed. In the future, our research will explore other similarity-calculation models to further accelerate processing times. Additionally, we aim to integrate privacy-preserving techniques to ensure secure dataset searches. These advancements are intended to enhance the practicality and efficiency of spatial dataset searches in real-world applications. Full article

Food security is an important foundation of national security. Since China entered a new era in 2012, the supply of agricultural and animal husbandry products in Qinghai has continuously enhanced. However, the implementation of ecological policies such as Grain for Green and Grassland Ecological Compensation restricted the cultivation and grazing areas. At the same time, with the improvement in living standards and food consumption demand of local residents, the contradiction between human beings and land has become increasingly prominent. It is necessary to analyze the balance between food supply and demand to evaluate food security. This study used supply–demand analysis and spatial autocorrelation analysis based on county-level statistical data on production and consumption collected through random sampling surveys to reveal the characteristics of the production and consumption of the main food types in Qinghai during 2012–2022 as well as to analyze the food self-sufficiency changes and their spatial clustering features. The results showed that the regions with higher grain and meat production in Qinghai were concentrated in the northeast in the past decade, while the regions with higher consumption were mainly in the counties with larger populations. At the county level, grain could not achieve self-sufficiency, except in northeastern Qinghai; meat was self-sufficient in most counties. Through regional allocation, Qinghai had achieved grain and meat self-sufficiency at the provincial level. The self-sufficiency of grain and meat showed obvious clustering, with high-value clusters of grain self-sufficiency and low-value clusters of meat both distributed in the provincial capital and surrounding areas, which were related to the adjustment of urban residents’ dietary structure from staple foods to diversified foods. This study provides a scientific basis for decision makers when adjusting the agricultural and animal husbandry structure as well as the dietary structure of residents to ensure food security and the sustainable utilization of land resources. Full article

Security vulnerabilities in IoT firmware pose significant risks, and black-box protocol fuzzing offers a scalable and cost-effective solution for identifying these vulnerabilities. However, current fuzzing evaluation schemes are often beset by challenges such as limited benchmark dataset sizes, low benchmark production efficiency, insufficient automation, and poor adaptability, which hinder cross-method comparisons. To address these challenges, we propose IoTBenchSL, a modular, pipeline-driven framework for constructing standardized, reproducible, and integrable vulnerability detection benchmark datasets. IoTBenchSL introduces a benchmark production workflow alongside a CI/CD-based automated validation pipeline, equipping each benchmark with a one-click emulation environment, automated validation mechanisms, and exploit verification scripts. This approach enhances the efficiency and accuracy of benchmark generation. Additionally, IoTBenchSL seamlessly integrates with existing fuzzing frameworks, enabling large-scale fuzzing evaluations and vulnerability discovery without modifying fuzzers. In our experiments, IoTBenchSL was used to generate 100 firmware benchmarks and 100 vulnerability benchmarks. We identified 32 previously unknown vulnerabilities through these benchmarks, 21 of which received CVE or CNVD identifiers. These results demonstrate IoTBenchSL’s capacity to generate large-scale IoT fuzzing benchmarks, improving evaluation efficiency and reducing costs for comparative studies, thereby advancing reproducible fuzzing research. Full article

Water ecological security is intricately connected to regional economic development and human survival, exerting a profound influence on regional sustainability. Water ecological security in the study area is a matter of urgency. In this study, the socio-economic, ecological, and water resource data of five cities in the west of the river from 2006 to 2021 are used to construct a dynamic model of the regional water ecological security system and simulate the trend of the regional water ecological security from 2022 to 2035, and the results indicate the following: (1) From 2006 to 2021, Hexi Corridor’s economy exhibited a significant upward trend, while its total population experienced a marked decline. Indicators for the ecological environment system showed notable improvement, whereas those for the water resource system demonstrated a significant downward trend. (2) Spatially, the mean values of system indices in the southeast and northwest regions were higher than those in the central region. (3) Between 2022 and 2035, projections reveal that the total GDP, industrial added value, average sewage discharge, urban green space, and water consumption for ecological purposes will all trend upward. Concurrently, the total population, total water supply, and total water demand are expected to exhibit a continuous decline. (4) The comparative comprehensive scores of the scenario models are as follows: EPS (2.18) > RSS (1.57) > CDS (1.15) > EDS (1.08). This analysis provides valuable insights into the dynamics of water ecological security in the Hexi Corridor and offers critical guidance for sustainable regional development planning. Full article

The expansion of operation technology (OT) use and its tight integration with classical information and communication technologies have led not only to additional and improved possibilities in monitoring physical/manufacturing processes and the emergency of Industry 4.0 but also to a number of new threats, both related to the security of processed data and the safety of people, affected by physical processes and controlled by OT. Understanding potential threats has caused an increased demand for scientific research in the field, which is still relatively new and lacks established terminology. In this review paper, we aim to identify emerging trends and technologies in OT incident response, attack detection, applications of machine and deep learning for attack recognition, and security of OT protocols. An examination of research patterns from the Web of Science repository is performed to comprehend the panorama of publications and the present state of research in the area of OT security. The analysis shows a notable rise in publications concerning OT security, reflecting an increasing research interest. Proceeding articles and research articles were the predominant types of publications that were analyzed. The analysis further emphasizes the collaborative connections between researchers, academic institutions, and nations. Additionally, co-occurrence and citation analyses are carried out to offer an understanding of the associations between various keywords and/or research subjects. The study is finalized by suggesting future research directions on OT security. The uniqueness of this review lies in its focus on OT rather than the more commonly explored SCADA/ICS topics, attempting to cover a wider range of research topics instead of concentrating on a narrow area/method. Full article

This research paper presents novel approaches for detecting credit card risk through the utilization of Long Short-Term Memory (LSTM) networks and XGBoost algorithms. Facing the challenge of securing credit card transactions, this study explores the potential of LSTM networks for their ability to understand sequential dependencies in transaction data. This research sheds light on which model is more effective in addressing the challenges posed by imbalanced datasets in credit risk assessment. The methodology utilized for imbalanced datasets includes the use of the Synthetic Minority Oversampling Technique (SMOTE) to address any imbalance in class distribution. This paper conducts an extensive literature review, comparing various machine learning methods, and proposes an innovative framework that compares LSTM with XGBoost to improve fraud detection accuracy. LSTM, a recurrent neural network renowned for its ability to capture temporal dependencies within sequences of transactions, is compared with XGBoost, a formidable ensemble learning algorithm that enhances feature-based classification. By meticulously carrying out preprocessing tasks, constructing competent training models, and implementing ensemble techniques, our proposed framework demonstrates unwavering performance in accurately identifying fraudulent transactions. The comparison of LSTM and XGBoost shows that LSTM is more effective for our imbalanced dataset. Compared with XGBOOST’s 97% accuracy, LSTM’s accuracy is 99%. The final result emphasizes how crucial it is to select the optimal algorithm based on particular criteria within financial concerns, which will ultimately result in more reliable and knowledgeable credit score decisions. Full article

With the rapid development of quantum computers and quantum computing, Internet of Things (IoT) networks equipped with traditional cryptographic algorithms have become very weak against quantum attacks. This paper focuses on the privacy-preserving problem in IoT networks and proposes a certificateless ring signature (CLRS) scheme. This CLRS is constructed with lattice theories, which show promising advantages in resisting quantum attacks. Meanwhile, the certificateless mechanism reduces the key control ability of the key generation center (KGC) by adding personal secret keys to the private key generated by the system. Meanwhile, the ring signature mechanism protects users’ privacy information through a non-central control mechanism. Next, the security proof in a random oracle model is given, which shows that this CLRS scheme can obtain unforgeability and ensure the signer’s anonymity. Its security properties include non-repudiation, traceability, and post-quantum security. Then, the efficiency comparison and performance results show that this CLRS scheme is more efficient and practical than similar schemes. Moreover, this work presents an exploration of the post-quantum cryptographic algorithm and its application in IoT networks. Full article

Mega-facility management has long been inefficient due to manual, reactive approaches. Current facility management systems face challenges such as fragmented data integration, limited predictive systems, use of traditional methods, and lack of knowledge of new technologies, such as Building Information Modeling and Artificial Intelligence. This study examines the transformative integration of Artificial Intelligence and Digital Twin technologies into Building Information Modeling (BIM) frameworks using IoT sensors for real-time data collection and predictive analytics. Unlike previous research, this study uses case studies and simulation models for dynamic data integration and scenario-based analyses. Key findings show a significant reduction in maintenance costs (25%) and energy consumption (20%), as well as increased asset utilization and operational efficiency. With an F1-score of more than 90%, the system shows excellent predictive accuracy for equipment failures and energy forecasting. Practical applications in hospitals and airports demonstrate the developed ability of the platform to integrate the Internet of Things and Building Information Modeling technologies, shifting facilities management from being reactive to proactive. This paper presents a demo platform that integrates BIM with Digital Twins to improve the predictive maintenance of HVAC systems, equipment, security systems, etc., by recording data from different assets, which helps streamline asset management, enhance energy efficiency, and support decision-making for the buildings’ critical systems. Full article

The fourth industrial revolution has ushered in a new era in which technology is seamlessly integrated into daily life. The digital transformation has created new media formats that require the development of robust digital skills to navigate this landscape. By utilising the Youth Digital Skills Indicator (yDSI) and integrating it with the Digital Competence Framework for Citizens (DigComp 2.2), this research examines media habits and digital competences among Croatian youth aged 10–24, corresponding to Generations Alpha and Z. A sample of 231 participants across three competence domains—information literacy, security and communication—revealed statistically significant generational differences in the first two areas of digital skills. Furthermore, gender-based analyses, conducted using the Mann–Whitney U-test and Spearman correlations for Likert scale responses, showed no significant differences. These findings deepen our understanding of digital natives, how media habits evolve and influence their digital skills, highlighting the need for more tailored strategies to enhance their competences and bridge generational gaps. Full article

Private information retrieval (PIR) enables a client to retrieve a specific element from a server’s database without disclosing the index that was queried. This work introduces three improvements to the efficient single-server PIR protocol Spiral. We found that performing a modulus switching towards expanded ciphertexts can improve the server throughput. Secondly, we apply two techniques called the composite $\mathsf{NTT}$ algorithm and approximate decomposition to Spiral to further improve it. We conduct comprehensive experiments to evaluate the concrete performance of our protocol, and the results confirm an approximately 1.7 times faster overall throughput than Spiral. Full article

End-to-end scene text spotting methods have garnered significant research attention due to their promising results. However, most existing approaches are not well suited for real-world applications because of their inherently complex pipelines. In this paper, we propose an end-to-end Character Region Excavation Network (CRENet) to streamline the text spotting pipeline. Our contributions are threefold: (i) Pipeline simplification: For the first time, we eliminate the text region retrieval step, allowing characters to be directly spotted from scene images. (ii) ROA layer: We introduce a novel RoI (Region of Interest) feature sampling layer for multi-oriented character region feature sampling, significantly enhancing the recognizer’s performance. (iii) Progressive learning strategy: We propose a progressive learning strategy to gradually bridge the gap between synthetic data and real-world images, addressing the challenge posed by the high cost of character-level annotations required during training. Extensive experiments demonstrate that our proposed method is robust and effective across horizontal, oriented, and curved text, achieving results comparable to state-of-the-art methods on ICDAR 2013, ICDAR 2015, Total-Text and ReCTS. Full article

The quarantine pest, Cylas formicarius, is a key pest of sweet potatoes during both production and storage, posing a major threat to food security in various countries. To investigate behavioral mechanisms, the ultrastructure of the heads of larval and adult stages was analyzed using scanning electron microscopy, with an emphasis on the sensilla of the mouthparts and antennae. The results reveal degeneration of the antennae and ocelli in larvae. The larval mouthparts are equipped with three types and six subtypes of sensilla. Both male and female adults have four types and six subtypes of sensilla on their mouthparts. Compared to larvae, the adult mouthparts display a greater diversity of sensilla types and higher numbers of sensilla basicaonica (SB), sensilla chaetica (SC), and sensilla digitiformia (SD). Adult antennae consist of a scape, a pedicel, and eight flagellomeres (F1–F8), with F8 showing sexual dimorphism. Seven types of sensilla, excluding SB and sensilla ligulate (SL), each with two subtypes, were identified on the antennae of adults of both sexes. SC, sensilla furcatea, Böhm bristles, and SL were newly observed in the antennae of C. formicarius adults. Additionally, one type and seven subtypes of sensilla on the adult antennae exhibit distinct sexual dimorphism in terms of structure or number. The relationship between the head structure and adaptability of C. formicarius was examined, and the functions of each sensilla were discussed, providing a theoretical basis for future studies on the behavior of this pest. Full article

Speculative execution attacks are serious security problems that cause information leakage in computer systems by building speculative covert channels. Hardware defenses mitigate speculative covert channels through microarchitectural changes. However, two main limitations become the major bottleneck in existing hardware defenses. High-security hardware defenses, such as eager delay, can effectively mitigate both known and unknown covert channels. However, these defenses incur high performance overhead due to the long-fixed delayed execution applied in all potential attack scenarios. In contrast, hardware defenses with low performance overhead are faster and can mitigate known covert channels, but lack sufficient security to mitigate unknown covert channels. The limitations indicate that it is difficult to achieve better security and performance of a processor against speculative execution attacks using a single defense method. In this paper, we propose ConBOOM, a configurable central processing unit (CPU) microarchitecture that provides optimized switchable hardware defensive modes, including the high-security eager delay mode and two proposed performance-optimized modes based on the anticipated attack scenarios. The defensive modes allow for flexibility in mitigating different speculative execution attacks with better performance, unlike the existing defenses having fixed performance overhead for all attack scenarios. The ConBOOM modes can be switched without modifying the hardware, and switching ConBOOM to the suitable mode for the anticipated attack scenario is achieved through the provided software configuration interface. We implemented ConBOOM on Berkeley’s RISC-V out-of-order processor core (SonicBOOM). Furthermore, we evaluated ConBOOM on the VCU118 FPGA platform. Compared to the existing representative work with the fixed performance overhead of 39.1%, ConBOOM has the lower performance overhead ranging between 15.1% and 39.1% to mitigate different attack scenarios. ConBOOM provides more defensive flexibility with negligible hardware resource overhead about 2.0% and good security. Full article