Search Results (29)

With the continuous expansion of data trading, the data supply chain system has gradually developed and improved. However, frequent security issues during the data transaction process have seriously hindered the development of the digital economy. As a key link in the data supply chain, the data trading market needs to use blockchain technology to achieve full-chain supervision of the data supply chain, which has become a top priority. Based on prospect theory, this paper constructs an evolutionary game model composed of data suppliers, consumers and data trading markets at all levels. The main factors affecting the system game strategy are discussed. The results show that: (1) The development of the data supply chain system can be divided into three stages, and blockchain technology plays a key role in realizing full-chain supervision of the data transaction process. The costs of blockchain adoption, market rewards, and penalties significantly affect the behavior of all parties. (2) The behavior of data suppliers has strong negative externalities and affects other participants. In addition, the larger the size of the data transaction, the lower the probability of breach by the data provider. (3) Adopting blockchain technology and implementing effective incentives can promote the development of the data supply chain. Full article

Earth-rock dams are critical components of hydraulic engineering, undertaking core functions such as flood control and disaster mitigation. However, the potential occurrence of dam breach poses a severe threat to regional socioeconomic stability and ecological security. To address the limitations of traditional Bayesian network (BN) in capturing the complex nonlinear coupling and dynamic mutual interactions among risk factors, they are integrated with machine learning techniques, based on a collected dataset of earth-rock dam breach case samples, the PC structure learning algorithm was employed to preliminarily uncover risk associations. The dataset was compiled from public databases, including the U.S. Army Corps of Engineers (USACE) and Dam Safety Management Center of the Ministry of Water Resources of China, as well as engineering reports from provincial water conservancy departments in China and Europe. Expert knowledge was integrated to optimize the network topology, thereby correcting causal relationships inconsistent with engineering mechanisms. The results indicate that the established hybrid model achieved AUC, accuracy, and F1-Score values of 0.887, 0.895, and 0.899, respectively, significantly outperforming the data-driven model G1. Forward inference identified the key drivers elevating breach risk. Conversely, backward inference revealed that overtopping was the direct failure mode with the highest probability of occurrence and the greatest contribution. The integration of data-driven approaches and domain knowledge provides theoretical and technical support for the probabilistic quantification of earth-rock dam breach and risk prevention and control decision-making. Full article

The rapid development of an increasing volume of web apps and the improper testing of the resulting code invariably provide more attack surfaces to potentially exploit. This leads to higher chances of facing cybersecurity breaches that can negatively impact both the users and providers of web services. Moreover, current data leaks resulting from breaches are most probably the fuel of future breaches and social engineering attacks. Given the context, a better analysis and understanding of web attacks are of the utmost priority. Our study provides practical insights into developing, implementing, deploying, and actively monitoring a web application-agnostic honeypot with the objective of improving the odds of defending against web attacks. Full article

Spam emails pose ongoing risks to digital security, including data breaches, privacy violations, and financial losses. Addressing the limitations of traditional detection systems in terms of accuracy, adaptability, and resilience remains a significant challenge. In this paper, we propose a hybrid spam detection framework that integrates a classical multinomial naive Bayes classifier with a pre-trained large language model, DeBERTa. The framework employs a weighted probability fusion strategy to combine the strengths of both models—lexical pattern recognition and deep semantic understanding—into a unified decision process. We evaluate the proposed method on a widely used spam dataset. Experimental results demonstrate that the hybrid model achieves superior performance in terms of accuracy and robustness when compared with other classifiers. The findings support the effectiveness of hybrid modeling in advancing spam detection techniques. Full article

Edge processing is a trend in developing new technologies that leverage Artificial Intelligence (AI) without transmitting large volumes of data to centralized processing services. This technique is particularly relevant for security applications where there is a need to reduce the probability of intrusion or data breaches and to decentralize alert systems. Although drone detection has received great research attention, the ability to identify helicopters expands the spectrum of aerial threats that can be detected. In this work, we present the development of a perimeter alert system that integrates AI and multiple sensors processed at the edge. The proposed system can be integrated into a LoRa or LoRaWAN network powered by solar energy. The system incorporates a PDM microphone based on an Arduino Nano 33 BLE with a trained model to identify a drone or a UH-60 from an audio spectrogram to demonstrate its functionality. It is complemented by two PIR motion sensors and a microwave sensor with a range of up to 11 m. Additionally, the DC magnetic field is measured to identify possible sensor movements or changes caused by large bodies, and a configurable RGB light signal visually indicates motion or sound detection. The monitoring system communicates with a second MCU integrated with a LoRa or LoRaWAN communication module, enabling information transmission over distances of up to several kilometers. The system is powered by a LiPo battery, which is recharged using solar energy. The perimeter alert system offers numerous advantages, including edge processing for enhanced data privacy and reduced latency, integrating multiple sensors for increased accuracy, and a decentralized approach to improving security. Its compatibility with LoRa or LoRaWAN networks enables long-range communication, while solar-powered operation reduces environmental impact. These features position the perimeter alert system as a versatile and powerful solution for various applications, including border control, private property protection, and critical infrastructure monitoring. The evaluation results show notable progress in the acoustic detection of helicopters and drones under controlled conditions. Finally, all the original data presented in the study are openly available in an OSF repository. Full article

The surface behaviours of humpback whales were studied in the presence of a whale-watching vessel at Nosy Be (Madagascar) during whale-watching activities, in order to characterise the ethogram of these animals. Data were collected from July to October 2018. Of the 75 total trips, humpback whales were observed 68 times and different types of aggregations were observed: Groups (33.82%), Mother–calf pairs (30.88%), Singles (27.94%), and Mother–calf and Escorts (7.35%). Individuals exhibited the following behaviours: Spouting, Breaching, Head Slap, Tail Throw, Tail Slap, Peck Slap, Spy-hopping, and Logging. Sighting data were evaluated by comparing the observed aggregations with reported behaviours, and vice versa. Among the most commonly observed behaviours, Spouting and Peck Slap were exhibited more in Groups, while Breaching was exhibited by all of the associations, with the exception of Singles. In Groups of more than two individuals, little or no social nor aggressive behaviours were observed, probably due to a lack of needing to attract the attention of other individuals. This suggests that, during the breeding season, Nosy Be could represent a wintering and weaning ground for calves. Full article

Autonomous unmanned aerial vehicles (UAVs) have several advantages in various fields, including disaster relief, aerial photography and videography, mapping and surveying, farming, as well as defense and public usage. However, there is a growing probability that UAVs could be misused to breach vital locations such as airports and power plants without authorization, endangering public safety. Because of this, it is critical to accurately and swiftly identify different types of UAVs to prevent their misuse and prevent security issues arising from unauthorized access. In recent years, machine learning (ML) algorithms have shown promise in automatically addressing the aforementioned concerns and providing accurate detection and classification of UAVs across a broad range. This technology is considered highly promising for UAV systems. In this survey, we describe the recent use of various UAV detection and classification technologies based on ML and deep learning (DL) algorithms. Four types of UAV detection and classification technologies based on ML are considered in this survey: radio frequency-based UAV detection, visual data (images/video)-based UAV detection, acoustic/sound-based UAV detection, and radar-based UAV detection. Additionally, this survey report explores hybrid sensor- and reinforcement learning-based UAV detection and classification using ML. Furthermore, we consider method challenges, solutions, and possible future research directions for ML-based UAV detection. Moreover, the dataset information of UAV detection and classification technologies is extensively explored. This investigation holds potential as a study for current UAV detection and classification research, particularly for ML- and DL-based UAV detection approaches. Full article

An archaeometallurgical study of a Renaissance breach pike was performed to elucidate its manufacturing process. Optical microscopy observations and microhardness measurements indicated that the breach pike was forged starting from a heterogeneous steel lump. The microstructural features were compatible with post-forging air cooling. The chemistry of a large set of nonmetallic inclusions was investigated by scanning electron microscopy coupled with X-ray dispersive spectroscopy. Compositional data were analyzed by multivariate statistics to distinguish smelting-related slag inclusions. A logistic regression model indicated that the steel was probably produced by the direct method. The liquidus temperatures of the slag inclusions indicated maximum smelting temperatures in the range of 1200 °C to 1300 °C. A thermodynamic-based model was adopted to estimate the average smelting conditions in terms of temperature and oxygen chemical potential and investigate the disequilibrium of slag inclusion–metal systems. For low-disequilibrium systems, the computed temperature values range between 1095 °C and 1118 °C, while the oxygen chemical potentials (μ_O2) span from −442 to −374 kJ/mol. Full article

Security log collection and storage are essential for organizations worldwide. Log analysis can help recognize probable security breaches and is often required by law. However, many organizations commission log management to Cloud Service Providers (CSPs), where the logs are collected, processed, and stored. Existing methods for log anomaly detection rely on unencrypted (plaintext) data, which can be a security risk. Logs often contain sensitive information about an organization or its customers. A more secure approach is always to keep logs encrypted (ciphertext). This paper presents “SigML++”, an extension of “SigML” for supervised log anomaly detection on encrypted data. SigML++ uses Fully Homomorphic Encryption (FHE) according to the Cheon–Kim–Kim–Song (CKKS) scheme to encrypt the logs and then uses an Artificial Neural Network (ANN) to approximate the sigmoid ($\sigma \left(x\right)$) activation function probabilistically for the intervals $[-10,10]$ and $[-50,50]$. This allows SigML++ to perform log anomaly detection without decrypting the logs. Experiments show that SigML++ can achieve better low-order polynomial approximations for Logistic Regression (LR) and Support Vector Machine (SVM) than existing methods. This makes SigML++ a promising new approach for secure log anomaly detection. Full article

In response to the challenges posed by rapid development, the wide-ranging disaster impact, and the untimely warning of debris flow resulting from tailing dam failure, it is of great significance to study the mechanism of dam failure as well as the evolution law and affected area of debris flow for effective disaster prediction and risk assessment. We developed a 1:150 physical model for testing tailing dam failure and combined it with RAMMS (V1.7.0) debris flow software to investigate the mechanisms of tailing dam failure and the evolutionary patterns of rock flows in complex environments. Through the analysis and comparison of experimental data, we comprehensively summarized the consequences of disaster risk resulting from dam failure. The results show that the grain size distribution of the model sand should be moderate; the composition of the particle size distribution has a significant impact on the collapse morphology of the dam after failure. The saturation line is the lifeline for the stability of the wake reservoir, and its level determines the degree of saturation of the wake in the reservoir. The breach was at the midpoint of the crest of the dam. The inflow volume at the time of the breach was 0.313 m³. According to the flow ratio relationship, the inflow volume at breach occurrence was equivalent to 1.78 times the total amount of a 1000-year flood and 1.19 times the total amount of a probable maximum flood (PMF). Analysis of the surface flow field revealed that the region with the highest flow velocity extended from the toe of the dam to Shangdi village; the impact on the village of Shizhou was limited to the backflow of the dam break and the gradual sedimentation of the tailings. Full article

Value at risk is a statistic used to anticipate the largest possible losses over a specific time frame and within some level of confidence, usually 95% or 99%. For risk management and regulators, it offers a solution for trustworthy quantitative risk management tools. VaR has become the most widely used and accepted indicator of downside risk. Today, commercial banks and financial institutions utilize it as a tool to estimate the size and probability of upcoming losses in portfolios and, as a result, to estimate and manage the degree of risk exposure. The goal is to obtain the average number of VaR “failures” or “breaches” (losses that are more than the VaR) as near to the target rate as possible. It is also desired that the losses be evenly distributed as possible. VaR can be modeled in a variety of ways. The simplest method is to estimate volatility based on prior returns according to the assumption that volatility is constant. Otherwise, the volatility process can be modeled using the GARCH model. Machine learning techniques have been used in recent years to carry out stock market forecasts based on historical time series. A machine learning system is often trained on an in-sample dataset, where it can adjust and improve specific hyperparameters in accordance with the underlying metric. The trained model is tested on an out-of-sample dataset. We compared the baselines for the VaR estimation of a day (d) according to different metrics (i) to their respective variants that included stock return forecast information of d and stock return data of the days before d and (ii) to a GARCH model that included return prediction information of d and stock return data of the days before d. Various strategies such as ARIMA and a proposed ensemble of regressors have been employed to predict stock returns. We observed that the versions of the univariate techniques and GARCH integrated with return predictions outperformed the baselines in four different marketplaces. Full article

It is important to determine the hydraulic boundary eigenvalues of typical embankment breaches before carrying out research on their occurrence mechanisms and assessing their repair technology. However, it is difficult to obtain the hydraulic boundary conditions of the typical levee breaches accurately with minor or incomplete measured data due to the complexity and instability of the levee breach. Based on more than 100 groups of domestic and foreign test data of embankment/earth dam failures, the correlation between the hydraulic boundary eigenvalues of a breach was established based on the cluster analysis approach. Additionally, the missing values were imputed after correlating and fitting. Meanwhile, the hydraulic boundary parameters and the related equations of a generalized typical breach were obtained through the statistical analysis of the probability density of the dimensionless eigenvalues of the breach. The analysis showed that the width of the breach mainly ranges in 20~100 m, while the water head of the breach is 4~12 m, and the velocity of the breach is 2~8 m/s. The distribution probabilities of all them are about 64~71%. The probability density of the width-to-depth ratio and the Froude number of the breach are both subject to normal distribution characteristics. The distribution frequency of the width-to-depth ratio of 3~8 is approximately 55%, and the Froude number of 0.4~0.8 is approximately 60%. These methods and findings might provide valuable support for the statistical research of the boundary and hydraulic characteristics of the breach, and the closure technology of breach. Full article

This research studies the occurrence of data breaches in healthcare provider settings regarding patient data. Using visual analytics and data visualization tools, we study the distribution of healthcare breaches by state. We review the main causes and types of breaches, as well as their impact on both providers and patients. The research shows a range of data breach victims. Network servers are the most popular location for common breaches, such as hacking and information technology (IT) incidents, unauthorized access, theft, loss, and improper disposal. We offer proactive recommendations to prepare for a breach. These include, but are not limited to, regulatory compliance, implementing policies and procedures, and monitoring network servers. Unfortunately, the results indicate that the probability of data breaches will continue to rise. Full article

Simulation of dam breach scenarios can help in the preparation of emergency action plans for real dam breaks or flash flooding events. The purpose of this study was to identify flood-prone areas in the Al Wala Valley in the governorate of Madaba in Jordan through analysis of the Al Wala Dam. Modelling of dam breaches was conducted under two scenarios: a Clear Day scenario and a Probable Maximum Flood (PMF) scenario. The former scenario does not address the various dam failure modes; rather, it addresses the formation and development of a breach as a result of structural failures like the sliding of dam blocks in the case of a concrete dam or piping failures in the case of embankment dams. The PMF scenarios, however, simulate unsteady flow in pipes and overtopping failure via consideration of runoff hydrography. In the PMF scenario, flood-prone areas can be identified by in-depth analysis of data from previous extreme rainfall events. The related hydrologic and hydraulic data can then be modelled using intensity-duration-frequency curves applied to an hour-by-hour simulation to discover the areas most at risk of flooding in the future. In the present study, data were collected from inlet of flow to Al Wala Valley on 10 January 2013. The collected data, which included rainfall and discharge data, were fed to the HEC-HMS software in order to calibrate the hydrological parameters of the watershed of the Al Wala Dam. Additionally, the HEC-RAS tool was employed to determine the breach outflow hydrography and hydraulic conditions across various critical downstream locations, which were determined by use of dynamic flood wave-routing models. The simulations revealed that, in the case of the Clear Day scenario, downstream inundation would cover an area of 5.262 km² in the event of a pipe failure. However, in the event of a six-hour storm, a twelve-hour storm, and a twenty-four-hour storm, the flooded area would rise to 6.837 km², 8.518 km², and 9.390 km², respectively. In the event of an overtopping failure, 13.171 km² would be inundated, according to the Clear Day scenario. On the other hand, in the event of a six-hour storm, a twelve-hour storm, and a twenty four-hour storm, the flooded area would rise to 13.302 km², 14.249 km², and 14.594 km², respectively. Full article

Blueberries are highly perishable and temperature sensitive. The main purpose of the study was to determine whether logistics processes in the beginning stages of the blueberry supply chain have an influence on the temperature profiles and quality of the fruit further downstream. Temperature trials were conducted on three farms in the Gauteng and three in the Western Cape provinces of South Africa. Observations were made, and iButton^® temperature monitoring devices were used to record ambient temperatures experienced by blueberries from harvesting until after forced cooling in the cold store. Descriptive statistics were used to analyse the temperature data. The results showed poor adherence to protocols and a large number of temperature and chilling injury spikes and breaks. Many trials did not reach pre-cooling and forced cooling protocol temperatures within the required time. Quality reports indicated that pallets were downgraded owing to cartons being underweight, probably as a result of moisture loss, and other quality defects such as collapsed berries and mould. By minimizing the breach of protocols and improving the beginning stages of the blueberry supply chain, a better-quality product will be ensured, thus reducing costs, food loss and food waste. Full article