Search Results (23)

This article focuses on the fire safety risks associated with conventional glass–aluminum façades—with a particular focus on stick and unitized curtain walling systems—providing an overview of possible fire spread mechanisms, considering the role of the curtain wall in maintaining compartmentation at the spandrel zone. First, it analyzes some of the relevant requirements of different European building regulations. Then, it provides a test evidence-based critical analysis of the gaps and loopholes in the relevant fire resistance standard for partial curtain wall configurations (EN 1364-4), where the evaluation of the propagation within the façade system is not necessarily considered in the fire-resistant spandrel zone. Finally, it presents a proposal for addressing these gaps in the form of a theoretical concept for a new test configuration and additional assessment criteria. This is followed by an initial experimental analysis of the concept. The standard testing campaign showed that temperature rise in mullions can exceed 180 °C after 30 min if limiting measures are not considered in the façade design. However, this can be only detected if framing is in the non-exposed area of the sample, being part of the evaluation surface. Meanwhile, differences are detected between the results from standard and new assessment criteria in the new configuration proposed, including a more rapid temperature rise for framing elements (207 K in a second level mullion at minute 90) than for the common non-exposed assessment surface of the sample (172 K at the same time) in cases where cavities are not protected. Accordingly, the proposed configuration successfully detected vertical temperature transfer within mullions, which can remain undetected in standard EN 1364-4 tests, highlighting the potential for fire spread even in EI120-rated assemblies. Full article

The automotive sector faces escalating security risks due to advances in wireless communication technology. Expanding on our previous research using a sensor pairing technique and machine learning models to evaluate IoT sensor data reliability, this study broadens its scope to address security concerns in Connected and Autonomous Vehicles (CAVs). The objectives of this research include identifying and mitigating specific security vulnerabilities related to CAVs, thereby establishing a comprehensive understanding of the risks these vehicles face. Additionally, our study introduces two innovative pairing approaches. The first approach focuses on pairing Electronic Control Units (ECUs) within individual vehicles, while the second extends to pairing entire vehicles, termed as vehicle pairing. Rigorous preprocessing of the dataset was carried out to ensure its readiness for subsequent model training. Leveraging Support Vector Machine (SVM) and TinyML methods for data validation and attack detection, we have been able to achieve an impressive accuracy rate of 97.2%. The proposed security approach notably contributes to the security of CAVs against potential cyber threats. The experimental setup demonstrates the practical application and effectiveness of TinyML in embedded systems within CAVs. Importantly, our proposed solution ensures that these security enhancements do not impose additional memory or network loads on the ECUs. This is accomplished by delegating the intensive cross-validation to the central module or Roadside Units (RSUs). This novel approach not only contributes to mitigating various security loopholes, but paves the way for scalable, efficient solutions for resource-constrained automotive systems. Full article

Web applications are widely used, and the applications deployed on the web do not always satisfy all the security policies. This may arise due to less secure configurations, less knowledge in security configurations, or due to insecure coding practices. Even though a lot of practices are available, a lot of security loopholes are still available for hackers to steal information. A secure web application framework is discussed here which incorporates solutions to major security loopholes that attackers may use for stealing information or compromising systems. The security framework proposed here ensures an encrypted data transfer making the data safe and server-side vulnerability detection and avoidance for major attacks like SQLinjection (SQLi) and Cross Site Scripting (XSS). The client side of the framework is responsible for validations, encryption, and session management through a JavaScript module. The server side of the framework is responsible for decryption and validation, data management, and URL management. The framework deployed with PHP showed a good outcome when tested with the Arachni web application security scanner. The framework will be further studied for performance with huge workloads. Further, the work will be extended to cover other attacks. Full article

Measurement-device-independent quantum key distribution (MDI-QKD) completely closes the security loopholes caused by the imperfection of devices at the detection terminal. Commonly, a symmetric MDI-QKD model is widely used in simulations and experiments. This scenario is far from a real quantum network, where the losses of channels connecting each user are quite different. To adapt such a feature, an asymmetric MDI-QKD model is proposed. How to improve the performance of asymmetric MDI-QKD also becomes an important research direction. In this work, an advantage distillation (AD) method is applied to further improve the performance of asymmetric MDI-QKD without changing the original system structure. Simulation results show that the AD method can improve the secret key rate and transmission distance, especially in the highly asymmetric cases. Therefore, this scheme will greatly promote the development of future MDI-QKD networks. Full article

In a sequence of papers, Marian Kupczynski has argued that Bell’s theorem can be circumvented if one takes correct account of contextual setting-dependent parameters describing measuring instruments. We show that this is not true. Despite first appearances, Kupczynksi’s concept of a contextual locally causal probabilistic model is mathematically a special case of a Bell local hidden variables model. Thus, even if one takes account of contextuality in the way he suggests, the Bell–CHSH inequality can still be derived. Violation thereof by quantum mechanics cannot be easily explained away: quantum mechanics and local realism (including Kupczynski’s claimed enlargement of the concept) are not compatible with one another. Further inspection shows that Kupczynski is actually falling back on the detection loophole. Since 2015, numerous loophole-free experiments have been performed, in which the Bell–CHSH inequality is violated, so, despite any other possible imperfections of such experiments, Kupczynski’s escape route for local realism is not available. Full article

Effective target detection and tracking has always been a research hotspot in the field of radar, and multi-target tracking is the focus of radar target tracking at present. In order to effectively deal with the issue of outlier removal and track initiation determination in the process of multi-target tracking, this paper proposes an improved backward smoothing method based on label iterative processing. This method corrects the loophole in the original backward smoothing method, which can cause estimated target values to be erroneously removed due to missing detection, so that it correctly removes outliers in target tracking. In addition, the proposed method also combines label iterative processing with track initiation determination to effectively eliminate invalid target short-lived tracks. The results of simulation experiments and actual data verification showed that the proposed method correctly removed outliers and invalid short-lived tracks. Compared with the original method, it improved the accuracy of target cardinality estimation and tracking performance to a certain extent. Moreover, without affecting the algorithm performance, the method’s processing efficiency could be improved by increasing the track pruning threshold. Finally, the proposed method was compared with existing methods, verifying that its tracking performance was better than that of existing methods. Full article

Active distributed generations (ADGs) are more prevalent near consumer premises. However, the ADG penetration contribute a lot of dynamic changes in power distribution networks which cause different protection and control issues. Islanding is one of the crucial problems related to such ADGs; on the other hand, islanding detection is also a challenging aspect. Therefore, an extensive review of islanding real-time depiction and islanding detection strategies (IDS) is provided in this work. Initially, the focus is on islanding detection concept depiction, islanding detection standardization, benchmark test systems for IDS validation, and software/tools and an analysis of their pros and cons. Then, the detailed classification of IDSs is presented with an emphasis on remote and local methods. Passive, active, and hybrid can be used further to categorize local IDSs. Moreover, the statistical comparative analysis of the IDSs based on the non-detection-zone (NDZ), cost-effectiveness, and false operation are mentioned. The research gap and loopholes in the existing work based on limitations in the existing work are presented. Finally, the paper is concluded with detailed recommendations. Full article

Deep neural networks (DNNs) have been employed in continuous-variable quantum key distribution (CV-QKD) systems as attacking detection portions of defense countermeasures. However, the vulnerability of DNNs leaves security loopholes for hacking attacks, for example, adversarial attacks. In this paper, we propose to implement the one-pixel attack in CV-QKD attack detection networks and accomplish the misclassification on a minimum perturbation. This approach is based on the differential evolution, which makes our attack algorithm fool multiple DNNs with the minimal inner information of target networks. The simulation and experimental results show that, in four different CV-QKD detection networks, $52.8\%$, $26.4\%$, $21.2\%$, and $23.8\%$ of the input data can be perturbed to another class by modifying just one feature, the same as one pixel for an image. We carry out this success rate in the context of the original accuracy reaching up to nearly $99\%$ on average. Further, by enlarging the number of perturbed features, the success rate can be raised to a satisfactory higher level of about $80\%$. According to our experimental results, most of the CV-QKD detection networks can be deceived by launching one-pixel attacks. Full article

Herbivore-induced plant volatiles (HIPVs) are released by plants upon damaged or disturbance by phytophagous insects. Plants emit HIPV signals not merely in reaction to tissue damage, but also in response to herbivore salivary secretions, oviposition, and excrement. Although certain volatile chemicals are retained in plant tissues and released rapidly upon damaged, others are synthesized de novo in response to herbivore feeding and emitted not only from damaged tissue but also from nearby by undamaged leaves. HIPVs can be used by predators and parasitoids to locate herbivores at different spatial scales. The HIPV-emitting spatial pattern is dynamic and heterogeneous in nature and influenced by the concentration, chemical makeup, breakdown of the emitted mixes and environmental elements (e.g., turbulence, wind and vegetation) which affect the foraging of biocontrol agents. In addition, sensory capability to detect volatiles and the physical ability to move towards the source were also different between natural enemy individuals. The impacts of HIPVs on arthropod natural enemies have been partially studied at spatial scales, that is why the functions of HIPVs is still subject under much debate. In this review, we summarized the current knowledge and loopholes regarding the role of HIPVs in tritrophic interactions at multiple scale levels. Therefore, we contend that closing these loopholes will make it much easier to use HIPVs for sustainable pest management in agriculture. Full article

The Data Distribution Service (DDS) for real-time systems is an industrial Internet communication protocol. Due to its distributed high reliability and the ability to transmit device data communication in real-time, it has been widely used in industry, medical care, transportation, and national defense. With the wide application of various protocols, protocol security has become a top priority. There are many studies on protocol security, but these studies lack a formal security assessment of protocols. Based on the above status, this paper evaluates and improves the security of the DDS protocol using a model detection method combining the Dolev–Yao attack model and the Coloring Petri Net (CPN) theory. Because of the security loopholes in the original protocol, a timestamp was introduced into the original protocol, and the shared key establishment process in the original protocol lacked fairness and consistency. We adopted a new establishment method to establish the shared secret and re-verified its security. The results show that the overall security of the protocol has been improved by 16.7% while effectively preventing current replay attack. Full article

Nowadays, water pollution has become a global issue affecting most countries in the world. Water quality should be monitored to alert authorities on water pollution, so that action can be taken quickly. The objective of the review is to study various conventional and modern methods of monitoring water quality to identify the strengths and weaknesses of the methods. The methods include the Internet of Things (IoT), virtual sensing, cyber-physical system (CPS), and optical techniques. In this review, water quality monitoring systems and process control in several countries, such as New Zealand, China, Serbia, Bangladesh, Malaysia, and India, are discussed. Conventional and modern methods are compared in terms of parameters, complexity, and reliability. Recent methods of water quality monitoring techniques are also reviewed to study any loopholes in modern methods. We found that CPS is suitable for monitoring water quality due to a good combination of physical and computational algorithms. Its embedded sensors, processors, and actuators can be designed to detect and interact with environments. We believe that conventional methods are costly and complex, whereas modern methods are also expensive but simpler with real-time detection. Traditional approaches are more time-consuming and expensive due to the high maintenance of laboratory facilities, involve chemical materials, and are inefficient for on-site monitoring applications. Apart from that, previous monitoring methods have issues in achieving a reliable measurement of water quality parameters in real time. There are still limitations in instruments for detecting pollutants and producing valuable information on water quality. Thus, the review is important in order to compare previous methods and to improve current water quality assessments in terms of reliability and cost-effectiveness. Full article

In most manufacturing industries, squirrel cage induction motors (SCIMs) are essential due to their robust nature, high torque generation, and low maintenance costs, so their failure often times affects productivity, profitability, reliability, etc. While various research studies presented techniques for addressing most of these machines’ prevailing issues, fault detection in cases of low slip or, low load, and no loading conditions for motor current signature analysis still remains a great concern. When compared to the impact on the machine at full load conditions, fault detection at low load conditions helps mitigate the impact of the damage on SCIM and reduces maintenance costs. Using stator current data from the SCIM’s direct online starter method, this study presents a feature engineering-aided fault classification method for SCIM at minor-load conditions based on a filter approach using the support vector classification (SVC) algorithm as the classifier. This method leverages the loop-hole of the Fourier Transform at minor-load conditions by harnessing the uniqueness of the Hilbert Transform (HT) to present a methodology that combines different feature engineering technologies to excite, extract, and select 10 discriminant information using a filter-based approach as the selection tool for fault classification. With the selected features, the SVC performed exceptionally well, with a significant diagnostic performance accuracy of 97.32%. Further testing with other well-known robust classifiers such as decision tree (DT), random forest (RF), k-nearest neighbor (KNN), gradient boost classifier (GBC), stochastic gradient descent (SGD), and global assessment metrics revealed that the SVC is reliable in terms of accuracy and computation speeds. Full article

The increased adoption of cloud computing resources produces major loopholes in cloud computing for cybersecurity attacks. An intrusion detection system (IDS) is one of the vital defenses against threats and attacks to cloud computing. Current IDSs encounter two challenges, namely, low accuracy and a high false alarm rate. Due to these challenges, additional efforts are required by network experts to respond to abnormal traffic alerts. To improve IDS efficiency in detecting abnormal network traffic, this work develops an IDS using a recurrent neural network based on gated recurrent units (GRUs) and improved long short-term memory (LSTM) through a computing unit to form Cu-LSTMGRU. The proposed system efficiently classifies the network flow instances as benign or malevolent. This system is examined using the most up-to-date dataset CICIDS2018. To further optimize computational complexity, the dataset is optimized through the Pearson correlation feature selection algorithm. The proposed model is evaluated using several metrics. The results show that the proposed model remarkably outperforms benchmarks by up to 12.045%. Therefore, the Cu-LSTMGRU model provides a high level of symmetry between cloud computing security and the detection of intrusions and malicious attacks. Full article

Although continuous-variable quantum key distribution (CVQKD) systems have unconditional security in theory, there are still many cyber attacking strategies proposed that exploit the loopholes of hardware devices and algorithms. At present, few studies have focused on attacks using algorithm vulnerabilities. The low-rate denial-of-service (LDoS) attack is precisely an algorithm-loophole based hacking strategy, which attacks by manipulating a channel’s transmittance T. In this paper, we take advantage of the feature that the power spectral density (PSD) of LDoS attacks in low frequency band is higher than normal traffic’s to detect whether there are LDoS attacks. We put forward a detection method based on the Bartlett spectral estimation approach and discuss its feasibility from two aspects, the estimation consistency and the detection accuracy. Our experiment results demonstrate that the method can effectively detect LDoS attacks and maintain the consistency of estimation. In addition, compared with the traditional method based on the wavelet transform and Hurst index estimations, our method has higher detection accuracy and stronger pertinence. We anticipate our method may provide an insight into how to detect an LDoS attack in a CVQKD system. Full article

The current crisis caused by the COVID-19 pandemic has hit the global economy hard, causing significant damage to every aspect of the global banking system, and Bangladesh is no exception. For that reason, its performance and profitability have been affected. In this study, we investigate the impact of COVID-19 on the financial performance and profitability of the listed private commercial banks in Bangladesh. We initially compute each bank’s financial performance index (FPI) to determine the position according to their financial performance individually before and the current period of COVID-19 by the standardized CAMELS rating system. After assessing the position, the fixed-effect regression model is used to explore the impact of the bank’s specific variables and macroeconomic variables along with the banks’ variables on the banks’ profitability. The banks that performed better during the pre-pandemic period of COVID-19 also performed better during the pandemic period of COVID-19. The performance of AIBL, EBL, and BBL was almost autonomously higher during both periods. In the case of bank profitability, our paper discovered that during the pandemic period of COVID-19, high non-performing loan rates, holding more liquid assets, a high amount of hedging capital, and inappropriate bank size lessened the banks’ profitability. In contrast, a low leverage position and inflation rate enhanced the bank’s profitability during this period. The outcome of this study will help bank authorities detect the loopholes and take preventive measures that can improve their profitability during a crisis period like COVID-19. The investors and depositors who invest money in banks can precisely decide their portfolios. Full article