User Access Research Articles

OPTIONS attack detection in WSN using Optimized Multitask Multi-Attention Residual Shrinkage Convolutional Neural Network

The rapid increase in Internet users has made web applications essential for daily services, rendering them targets for various cyber-attacks like path traversal, zero-day attacks, and injection attacks. While traditional security measures can prevent many familiar attacks, they are often ineffective against OPTIONS attacks, which involve injecting malicious code via hyperlinks to obstruct user access to legitimate webpage content. To address this novel challenge, we propose the OAD-WSN-MMRCNN technique, leveraging an Optimized Multitask Multi-Attention Residual Shrinkage Convolutional Neural Network for OPTIONS attack detection in Wireless Sensor Networks (WSNs). This approach begins by selecting a CPU parameters dataset for attack detection, followed by pre-processing with a Variational Bayesian-Based Maximum Correntropy Cubature Kalman Filter to remove redundant data. Key features such as handles, threads, processor, context switch, deferred procedure call (DPC), interrupt delta, CPU socket, and core are extracted using a variable velocity strategy particle swarm optimization algorithm. The MMRCNN, optimized with the Tyrannosaurus Optimization Algorithm, is then employed to detect normal and OPTIONS attacks. Implemented in Python, the efficacy of OAD-WSN-MMRCNN is evaluated using metrics such as energy consumption, target window, accuracy, precision, F-measure, recall, and CPU utilization. Experimental results demonstrate that OAD-WSN-MMRCNN outperforms existing techniques, achieving a 20 % improvement in detection accuracy and a 25 % reduction in energy consumption, highlighting its effectiveness and potential for enhancing web application cyber security.

Multidimensional Forensic Analysis of Onion Sites Using LSTM and Fuzzy Encoded Techniques: Insights and Applications in Digital Investigations

When it comes to digital investigations, the fact that onion sites make people anonymous makes investigative research much harder. For multidimensional forensic analysis of onion sites, this study suggests a new method that combines Long Short-Term Memory (LSTM) networks with fuzzy encoding methods. When you use the Tor network to get to onion sites, both users and services become anonymous, which makes it harder to do regular investigations. LSTM networks are used to look at trends in how people use the site and when material is updated over time. These networks are good at modeling sequential data. Fuzzy encoding methods work with LSTM to handle the uncertainty that comes with analyzing anonymous data, which makes investigative results more accurate. The method involves keeping time logs of what people do on onion sites and turning them into fuzzy sets to show how user interactions and content changes change over time. LSTM models are taught on these recorded sequences to find patterns that point to suspicious activity, like sudden changes in content or strange patterns of user access. This method can be used to find illegal activities, find secret services that host illegal content, and make profiles of how users behave in an anonymous network. Investigators can better track actions across onion sites when they use LSTM's ability to see how events depend on each other over time and fuzzy encoding's ability to deal with inaccurate data. Real-world onion site data used for experiments shows that the proposed method can find secret behaviors and patterns that are hard for traditional investigative methods to find. The results show that the accuracy and efficiency of detecting are much better than with traditional methods.

The Implementation of Titian for Data Provenance on DISC Systems Automated Debugging

Data-Intensive Scalable Computing (DISC) systems are critical for managing large datasets while prioritizing fault tolerance, cost effectiveness, and user accessibility. However, the presence of input errors in processed data presents considerable hurdles to programmers. The Snowfall Analysis program, which is well-known for its anomalous data that causes forecasting failures, serves as a key case study in this research. To solve this problem, this study leverages Titian, an extended library designed to speed debugging by methodically tracing the provenance of incorrect data back to its original source. Through thorough analysis, we analyzed Titian's accuracy using confusion matrix and compared its efficiency to standard manual debugging approaches, showing solid evidence of its utility in improving data provenance in DISC systems.

Security monitoring via sound analysis and voice identification with artificial intelligence

The article demonstrates the possibility of monitoring user access through authentication based on voice profiles using the means of Artificial Intelligence. A two-stage approach is proposed for sound analysis and voice recognition using Feed-Forward Neural Networks (FFNNs) and Cascade-Forward Neural Networks (CFNNs). Seven test voice profiles were pre-processed to extract quantitative sound features. The procedure involves registration of a set of sound parameters concerning three categories, respectively, for all audio and acoustic measurements in the entire sound spectrum, measurements up to and above 100 dBA. The neural architectures were trained with Scaled Gradient Descent (SCG) and Levenberg Marquardt (LM) algorithms, using different transfer functions in the output structural layers. In the initial phase of neural training, the entire sound spectrum of registered indicators was used, and high levels of Accuracy around 90.0% were reached. Subsequently, steps were taken to reduce the informative features when searching for similar levels of accuracy in order to limit the necessary computational procedures in neural training, but maintain the threshold of successful user authentication. In the analysis of neural performance, in addition to accuracy, additional criteria were used, namely Mean-Squared Error (MSE) and Root Mean Squared Error (RMSE). About the achieved and analyzed results, a synthesis was conducted of a set of four informative features with the highest significance, respectively LAE (A-weighted, sound exposure level), Laeq (A-weighted, equivalent sound level), LAF (A-weighted, fast time-constant, sound level) and LAS (A-weighted, slow time constant response, sound level). In the course of subsequent neural training processes, unsuitability was found when using the Log-sigmoid activation type with greatly underestimated accuracy readings and errors below 58.0% and above thresholds of 0.2300 and 0.4800. Positive performance indicators of voice recognition were achieved with Softmax and Hyperbolic tangent sigmoid activations in SCG and LM training procedures in levels of accuracy of 98.7 % and 96.1 % at FFNN models. Successful correct recognition of the test voice profiles on access and security personalization with a quantitative equivalent of 100.0 % accuracy was achieved in the Linear transfer function for Cascade-Forward Neural Networks. The proposed method and the synthesized neural models in the research can be used as units and modules in access control systems with biometric diagnostics and intelligent recognition of employees in company departments to electronically store classified information and physical access control.

TSAPP: Threshold Single-Sign-On Authentication Preserving Privacy

Single-sign-on (SSO) authentication enables a user to gain a token from the identity server, with which the user accesses multiple services. To address single-point-of-failure of SSO, threshold SSO, where a group of identity servers issue a user with a token in the threshold manner, is introduced. SSO including threshold schemes suffers from privacy disclosure. One can learn a user's identity and access pattern from her/his token. Recent works focus on privacy preservation of SSO. However, these works merely consider scenarios of one single identity server SSO. No works that address privacy preservation of threshold SSO have emerged. In this work, we propose TSAPP, a threshold SSO authentication scheme preserving privacy. Each identity server issues a user with a partial token which is a signature on the user's pseudonym. With a threshold number of partial tokens, the user constructs a token, blinds the token with random numbers and accesses services with blinded tokens. Such mechanism preserves the user's identity, simultaneously protects the user's access pattern since adversaries cannot link the user's accesses, even if identity servers are corrupted. Security analysis demonstrates that TSAPP satisfies properties of anonymity, unlinkability, unforgeability and password-safety. The performance evaluation demonstrates that TSAPP is efficient in practice.

QoS-Oriented uplink OFDMA random access for the next-gen WLAN

Abstract OFDMA, introduced by the IEEE 802.11ax standard, divides the channel into multiple resource units (RUs), enabling parallel access for multiple users. On this basis, we propose a QoS-oriented double arbitration random access (Q-DARA) scheme. This scheme ensures high-priority priority access through priority arbitration in the first phase and random code arbitration in the second phase. It can also greatly reduce collisions and effectively guarantee the QoS of high-priority traffic. Simulation shows that our proposed scheme has better performance on throughput and latency. Compared to the traditional EDCA, the throughput and latency increase by more than 70%. Compared to the PATB scheme, the throughput increases by about 20%, and the latency increases by about 23%. Moreover, our proposed scheme has almost no changes to the protocol and is suitable for 802.11ax and beyond.

THE CONCEPT OF NETWORK NEUTRALITY

Technology is meant to give people a platform to express themselves. Therefore, network neutrality is a creation for platform where contents, applications, services are to move on the internet without any form of restrictions. It is worrisome to some that internet providers are beginning to give unfair advantage to some data streams thereby making it almost difficult for smaller content creators and to grow. Network neutrality is the principle that internet provider and the Government should treat internet traffic the same. It connotes putting policy and regulatory measures in place to ensure open access and fair treatment of all internet users. This paper discusses the concept of network neutrality according to Tim Wu. It also considers the advantages and disadvantages of network neutrality. It discusses network neutrality in the United States and also the provisions of the Draft code released by the Nigerian Communication Commission in 2017. Hence, a doctrinal methodology was adopted using primary and secondary sources

Yearly Update of the List of Plant Diseases in Korea (6.2 Edition, 2024)

Since 2009, the Korean Society of Plant Pathology has established the Committee on Common Names of Plant Disease to systematically review and determine plant disease names and related terminologies. The committee published the 6th edition of the List of Plant Diseases in Korea (LPDK) in 2022, and the list has been made publicly accessible online. The online database has significantly enhanced user accessibility, expedited update processes, and improved interoperability with other databases. As a result, the 6.1 edition of the list was released by online LPDK in 2023, detailing new disease names added over the preceding year and revisions to existing names. Subsequently, in 2024, the 6.2 edition was published, encompassing 6,765 diseases caused by 2,503 pathogen taxa across 1,432 host species. The public release of the online database has, however, introduced several challenges and tasks. Addressing these issues necessitates the development of modern, standardized nomenclature guidelines and a robust system for the registration of new disease names. Open communication and collaboration among the diverse members of the Korean Society of Plant Pathology are required to ensure the reliability of the LPDK.

TRANSPORT SERVICE WEBSITE

Abstract: Transport is an awesome website that offers a wide range of shipping services. They specialize in transporting goods by ship, providing efficient and reliable solutions for both domestic and international shipping needs. Whether you need to transport large cargo or small packages, ShipEx has got you covered. Their website provides all the information you need about their services, including rates, tracking options, and customer reviews. It’s a convenient and trustworthy platform for all your shipping needs. Online transportation service has become a means of transportation development which able to attract people’s interest. This research explores the development and implementation of an intelligent transportation services website aimed at optimizing transportation services. Leveraging advanced algorithms, real-time data integration, and user-centric design, our platform facilitates seamless connections between various transportation modes, providing users with personalized, efficient, and sustainable goods transport options. Our findings highlight the positive impact of inclusive design on user satisfaction, accessibility, and overall usability, contributing to the broader discourse on creating equitable transportation solutions. This research explores the integration of augmented reality (AR) to improve user experience on transportation services websites. By overlaying real-time information onto the physical environment, our AR-enhanced platform provides users with intuitive navigation and enhanced situational awareness. We will ensure interactions and experience is top notch for the users. Index Term-: Management Processes, Transport Services, Strategic Functions, HTML, CSS, PHP.

Analyzing Communication and Satisfaction in Mental Health Online Consults for Students

Online mental health consultation applications have become increasingly prevalent in Indonesia. Teenagers, including college students use these applications to communicate their problems and seek solutions. This study aims to describe final year students' experiences in using these applications. This study evaluates the effectiveness of an online mental health consultation service through the perceptions of 118 users, utilizing a Likert scale across three key dimensions: usefulness, usability, also integration and infrastructure. The findings indicate a generally positive reception of the service. In the usefulness dimension, the service scored well in terms of meeting advertised expectations, maintaining consistent quality, fostering optimism, providing clarity on mental health issues, and making users feel at ease. The usability dimension highlighted the enjoyable nature of the application and its benefits, though it revealed a need for improvement in accessibility for users with disabilities. The integration and infrastructure dimension reflected positively on the service's intuitive design and user-friendliness, with high scores in data confidentiality, responsiveness of counsellors, clarity of communication, and effectiveness in addressing user issues. Users also expressed a willingness to reuse the service for future mental health needs, indicating its perceived value and reliability.

Broadening access to small-molecule parameterization with the force field toolkit.

Access to accurate force-field parameters for small molecules is crucial for computational studies of their interactions with proteins. Although a number of general force fields for small molecules exist, e.g., CGenFF, GAFF, and OPLS, they do not cover all common chemical groups and their combinations. The Force Field Toolkit (ffTK) provides a comprehensive graphical interface that streamlines the development of classical parameters for small molecules directly from quantum mechanical (QM) calculations, allowing for force-field generation for almost any chemical group and validation of the fit relative to the target data. ffTK relies on supported external software for the QM calculations, but it can generate the necessary QM input files and parse and analyze the QM output. In previous ffTK versions, support for Gaussian and ORCA QM packages was implemented. Here, we add support for Psi4, an open-source QM package free for all users, thereby broadening user access to ffTK. We also compare the parameter sets obtained with the new ffTK version using Gaussian, ORCA, and Psi4 for three molecules: pyrrolidine, n-propylammonium cation, and chlorobenzene. Despite minor differences between the resulting parameter sets for each compound, most prominently in the dihedral and improper terms, we show that conformational distributions sampled in molecular dynamics simulations using these parameter sets are quite comparable.

FLYWISE AIRWAYS WEBSITE

Abstract— The FLYWISE Airways website is a user-centric platform designed to streamline the booking process and enhance the overall travel experience for passengers. With its intuitive interface and robust functionality, the website allows users to easily search for flights, compare fares, and make secure online bookings. In addition to booking flights, the website also provides valuable information about FLYWISE Airways’ services, fleet, and destinations. The website’s responsive design ensures a seamless experience across all devices, making it easy for passengers to access the information they need, whenever they need it. With its focus on user convenience and accessibility, the FLYWISE Airways website is poised to become the go-to platform for travelers seeking a hassle-free booking experience. The FLYWISE Airways website is designed with mobile responsiveness in mind, ensuring that passengers can access the website from any device, including smartphones and tablets. This ensures that passengers can easily book flights and access important information while on the go. Keywords – Flight booking, air travel, online reservations, flight search, compare fares, secure booking, flight information, manage bookings, seat selection, extra baggage, in-flight meals, frequent flyer program, mobile responsiveness, user-friendly interface.

A Curvilinear Transfer Function for Wide Dynamic Range Compression with Expansion

Wide Dynamic Range Compression in hearing aids is becoming increasingly more complex as the number of channels and adjustable parameters grow. At the same time, there is growing demand for customization and user self-adjustment of hearing aids, necessitating a balance between complexity and user accessibility. Compression in hearing aids is governed by the input-output transfer function, which relates input magnitude to output magnitude, and is typically defined as a combination of linear piecewise segments resembling logarithmic behavior. This work presents an alternative to the conventional compression transfer function that consolidates multiple compression parameters and revisits expansion in hearing aids. The curvilinear transfer function is a continuous curve with logarithm-like behavior, governed by two parameters—gain and compression ratio. Experimental results show that curvilinear compression reduces the amplification of low-level noise, improves signal-to-noise ratio by up to 1.0 dB, improves sound quality as measured by the Hearing Aids Speech Quality Index by up to 6.7%, and provides comparable intelligibility as measured by the Hearing Aids Speech Perception Index, with simplified parameterization compared to conventional compression. The consolidated curvilinear transfer function is highly applicable to over-the-counter hearing aids and offers more capabilities for customization than current prominent over-the-counter and self-adjusted hearing aids.

Real‐Time Air Quality Index Detection through Regression‐Based Convolutional Neural Network Model on Captured Images

ABSTRACTOur research addresses the imperative need for an efficient air quality monitoring and forecasting system to mitigate the significant health risks of air pollution. Departing from conventional binary data collection methods, we employ image‐based techniques to overcome inherent limitations. A pioneering aspect of our work involves the development of a novel model capable of predicting six air pollutants (PM2.5, PM10, O3, CO, SO2, NO2) along with Air Quality Index for Bengaluru, Delhi, and Tamil Nadu, achieving a commendable mean absolute error of 0.1432 on the test set. The efficacy of our approach is validated through a meticulously curated dataset comprising approximately 5455 images. We emphasize the significance of normalization by presenting outputs before and after, shedding light on the impact of parameters with varying ranges and strategies employed to mitigate such discrepancies. A detailed analysis of our model's best and worst outputs provides valuable insights into its strengths and limitations. To enhance user accessibility, we introduce an innovative image‐based, real‐time, user‐friendly dashboard that allows users to conveniently assess a location's air pollution levels by uploading an image. This holistic approach offers a promising avenue for accurate air quality prediction and real‐time monitoring.

ANALISIS EFISIENSI DAN KEAMANAN KONFIGURASI WEB PROXY DALAM MENGELOLA TRAFIK INTERNET DI LINGKUNGAN PENDIDIKAN

Internet use in educational settings is increasing, but can cause problems such as access to inappropriate content, the spread of malware, and misuse of bandwidth. Web proxies can be a solution for managing internet traffic efficiently and safely. This article analyzes the efficiency and security of web proxy configurations in managing internet traffic in educational environments. The efficiency of web proxy configuration is influenced by the type of web proxy, cache configuration, and bandwidth configuration. The security of a web proxy configuration is affected by authentication, content filtration, and software updates. Proper web proxy configuration can increase the efficiency and security of internet access for users in educational environments.

A Sustainable Solution for Urban Transport Using Photovoltaic Electric Vehicle Charging Stations: A Case Study of the City of Hail in Saudi Arabia

As the global shift toward sustainable transportation gains momentum, the integration of electric vehicles (EVs) becomes imperative, necessitating a robust and environmentally friendly charging infrastructure. Leveraging the abundant solar potential in the region, this study examines the technical, economic, and environmental feasibility of deploying photovoltaic electric vehicle charging stations (PV-EVCSs) in Hail City, Saudi Arabia, as a case study. This study examines factors such as the energy demand, grid integration, and user accessibility, aiming to address the challenges and opportunities presented by the urban fabric. The proposed solar charging station network seeks to catalyze a paradigm shift toward a cleaner and more sustainable transportation ecosystem, embodying a forward-thinking approach to meeting the evolving needs of urban mobility in the 21st century. The analysis encompasses many scenarios, encompassing a range of car battery sizes, charger powers, and car slots per station. Zone 4 is identified as the most crucial area, where seven charging stations are needed to fulfill the expected demand in the absence of any private charging alternatives. The economic evaluation of the 1047.35 kWp PV system reveals an estimated conventional payback time of 11.69 years, accompanied by a return on assets of 10.17%. The system generates accumulated cash flows amounting to SR 7,169,294.62 over 30 years, while the estimated operational and maintenance expenses are predicted to be SR 50,000 per year. The overall investment cost for the solar PV and EV charging stations is SR 4,487,982. This cost is offset by the yearly electricity savings from solar and grid sources, which can reach up to SR 396,465.26 by year 30. This work presents a detailed plan for the future of sustainable transport. It combines technical, environmental, and economic aspects to promote a cleaner and more sustainable urban mobility system.

Single Window Search System for Libraries using VuFind

The main objective of this study is to develop methods for enhancing the search capabilities of library collections by integrating various technological elements. Implementing this functional enhancement will provide users access to modern services and resources. The single-window search mechanism is created and built on the VuFind open-source discovery tool. The common access window has been created after properly configuring ILS-OPAC Koha, CONTENTdm, Digital Commons, DSpace, EPrints, Fedora, Greenstone, Omeka, and OPUS. It is based on LAMP architecture and uses Apache Solr, Tomcat, and Apache Tika to create indexes for easy document search through VuFind. This study suggests a new way to easily search and collect data from different domain-specific cluster tools using a single interface. This domain-specific framework provides an ideal environment for libraries to develop large-scale library discovery services. The originality of this paper lies in creating a prototype integrated framework to facilitate the easy installation and configuration of VuFind for integrating domain-specific software in both ILS and IDR.

Human-Computer Interaction in Cloud Systems

As cloud computing continues to revolutionize the landscape of modern computing, the intersection of human-computer interaction (HCI) within cloud systems has become increasingly vital. This study aims to explore and analyze the evolving relationship between HCI principles and cloud computing environments. It investigates the various dimensions of HCI in cloud systems, encompassing user experience design, interface usability, accessibility, and collaboration paradigms. The paper begins by providing a comprehensive overview of HCI fundamentals, emphasizing their significance in the context of cloud computing. It delves into the unique challenges and opportunities presented by cloud environments, such as distributed computing, virtualization, and scalability concerns, and examines their implications forHCI design. Furthermore, the study investigates contemporary HCI methodologies and techniques tailored specifically for cloud systems, including adaptive interfaces, multi-platform compatibility, and responsive design principles. It discusses how these approaches aim to optimize user interaction, enhance productivity, and facilitate seamless user experiences across diverse devices and environments.

Deep learning enhancing guide RNA design for CRISPR/Cas12a-based diagnostics.

Rapid and accurate diagnostic tests are fundamental for improving patient outcomes and combating infectious diseases. The Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR) Cas12a-based detection system has emerged as a promising solution for on-site nucleic acid testing. Nonetheless, the effective design of CRISPR RNA (crRNA) for Cas12a-based detection remains challenging and time-consuming. In this study, we propose an enhanced crRNA design system with deep learning for Cas12a-mediated diagnostics, referred to as EasyDesign. This system employs an optimized convolutional neural network (CNN) prediction model, trained on a comprehensive data set comprising 11,496 experimentally validated Cas12a-based detection cases, encompassing a wide spectrum of prevalent pathogens, achieving Spearman's ρ = 0.812. We further assessed the model performance in crRNA design for four pathogens not included in the training data: Monkeypox Virus, Enterovirus 71, Coxsackievirus A16, and Listeria monocytogenes. The results demonstrated superior prediction performance compared to the traditional experiment screening. Furthermore, we have developed an interactive web server (https://crispr.zhejianglab.com/) that integrates EasyDesign with recombinase polymerase amplification (RPA) primer design, enhancing user accessibility. Through this web-based platform, we successfully designed optimal Cas12a crRNAs for six human papillomavirus (HPV) subtypes. Remarkably, all the top five predicted crRNAs for each HPV subtype exhibited robust fluorescent signals in CRISPR assays, thereby suggesting that the platform could effectively facilitate clinical sample testing. In conclusion, EasyDesign offers a rapid and reliable solution for crRNA design in Cas12a-based detection, which could serve as a valuable tool for clinical diagnostics and research applications.

Review of existing models and basic zero trust principles

Ensuring the information security of an enterprise is quite a complex task. This is due to the multifaceted nature of IT infrastructure and applications, the breadth and intensity of user access, the excessive openness of most corporate networks, and several other factors. In these conditions, the concept of zero trust is increasingly being considered as the most preferable solution to the problem of ensuring the security of enterprises, organizations, institutions. The basic idea of the concept of zero trust is that there are no areas that are trustworthy. However, despite the popularization of the zero trust concept and the obvious security benefits of its application in enterprises, there are certain difficulties in its implementation. In particular, planning to bring the infrastructure into compliance with the zero-trust principles cannot be accomplished partially or as part of minor modifications to the relevant information systems. It is necessary to reorganize the information infrastructure as a whole, as well as to integrate all aspects that ensure the security of enterprise activities, so that the zero-trust principles show their effectiveness. On the other hand, today there is a problem associated with a certain lack of awareness about the zero-trust approach (about its theoretical and practical potential) for choosing the right solution. This paper is precisely aimed at solving this problem by summarizing existing research and the experience of various international companies that are implementing this approach in practice. It briefly discusses models and key zero-trust principles proposed by renowned international organizations and companies that will help make sense of a fundamental shift in the approach to information security, cybersecurity.