Secure Platform Research Articles

The Role of API Security in Modern Enterprise Platforms

This article explores the critical role of API security in modern enterprise platforms, addressing the growing importance of protecting Application Programming Interfaces (APIs) as they become increasingly central to digital ecosystems. We examine the fundamental challenges organizations face in securing their APIs, including authentication, data encryption, rate limiting, and version management. The paper presents a comprehensive overview of best practices for API security, such as implementing OAuth and token-based authentication, utilizing Transport Layer Security (TLS), deploying API gateways, and conducting regular audits. We discuss implementation strategies that integrate security throughout the API development lifecycle and highlight the importance of developer training. Through case studies, we illustrate both successful API security implementations and lessons learned from security breaches. The article also looks ahead to future trends in API security, considering the impact of emerging technologies like AI and quantum computing, as well as the evolving threat landscape. By providing a thorough analysis of current practices and future directions, this paper aims to equip organizations with the knowledge needed to develop robust, secure, and scalable API infrastructures that can support innovation while protecting critical data and resources in an increasingly interconnected digital world.

Scalable Photo-Responsive Physical Unclonable Functions via Particle Kinetics.

The increasing menace of counterfeiting and information theft underscores the urgent need for security platforms compatible with both micro- and nanoelectronics. Existing methods for anticounterfeiting labeling and cryptographic systems rely on unclonable patterns derived from the unpredictable variability of physical phenomena. However, these approaches impose limitations on the scalability of security components. Here we present a scalable platform for photoresponsive physically unclonable functions based on oxide particle kinetics in polymer solutions. The stochastic agglomeration process occurring during the formation of polymer films with dispersed oxide particles yields random patterns, with pixel sizes scalable from micro to nanoscales. We produce mechanically flexible and self-destructible optical unclonable function patterns utilizing oxide aggregates on a polymer film. Moreover, we establish a strategy for generating electrical unclonable patterns on a conducting polymer film. This involves covering the polymer film with an aggregate pattern mask, which serves as an ultraviolet-blocking layer for randomly exposing the film to ultraviolet ozone treatment. These unclonable patterns constitute robust and compact security systems, exhibiting effective resilience against machine-learning attacks (∼50% prediction error for training data sets of 1000). The developed scalable platforms for physically unclonable functions provide a hardware solution for robust cryptographic applications.

ExamVoice: Innovative Solutions for Improving Exam Accessibility for Blind and Visually Impaired Students in Saudi Arabia

Ensuring equitable educational opportunities for blind and visually impaired (BVI) students in Saudi Arabian higher education remains challenging despite legislative and institutional efforts. This paper proposes a system to assist BVI students independently during exams, aiming to create an inclusive exam environment that minimizes distractions and facilitates their participation alongside peers. Key contributions include the development of a secure platform, ensuring exam confidentiality, and fostering confidence and active engagement among BVI individuals. The study highlights the importance of integrating innovative assistive technologies and implementing rigorous policies to overcome systemic barriers and improve support mechanisms for BVI students in educational settings.

Data Quality Based Intelligent Instrument Selection with Security Integration

We propose a novel Data Quality with Security (DQS) integrated instrumentation selection approach that facilitates aggregation of multi-modal data from heterogeneous sources. As our major contribution, we develop a framework that incorporates multiple levels of integration in finding the best DQS-based instrument selection: data fusion from multi-modal sensors embedded into heterogeneous platforms, using multiple quality and security metrics and knowledge integration. Our design addresses the security aspect in the instrumentation design, which is commonly overlooked in real applications, by aggregating it with other metrics into an integral DQS calculus. We develop DQS calculus that formalizes the problem of finding the optimal DQS value. We then propose a Genetic Algorithm–based solution to find an optimal set of sensors in terms of the DQS they provide, while maintaining the level of platform security desirable by the user. We show that our proposed algorithm demonstrates optimal real-time performance in multi-platform instrument selection. To facilitate the framework application by the instrumentation designers and users, we develop and make available multiple Android applications.

RideChain: A Blockchain-Based Decentralized Public Transportation Smart Wallet

The transportation industry has been recognized as one of the industries that can benefit from investment in blockchain-based systems and services that enable distributed data management and improve the effectiveness and efficiency of the transportation sector. However, the literature needs a guiding framework for integrating blockchain in issuing and preserving public transportation transactions in a technical environment that is secure, efficient, and transparent. This study proposes a blockchain-based transportation wallet (BTW) framework that facilitates the main digital transactions across diverse public transportation services. BTW embodies leveraging blockchain technology, which provides a decentralized and immutable ledger that records and verifies transactions, ensuring trust and reducing the risk of fraud. The framework has been validated by developing a blockchain-based public transportation smart wallet named “RideChain”. This serves as a single decentralized point for making public transportation transactions and payments, as well as identity authorizations and management. RideChain enhances passengers’ and service providers’ experience through a secure and authentic platform for offering several reliable public transportation transactions efficiently. In this study, we implemented a smart contract to establish a protocol between passengers and journey services. The testing methodologies used in this study comprise unit testing, integration testing, performance testing, and user acceptance testing. The findings suggest that BTW has been successfully verified to demonstrate its capability for secure transactions, authenticity of monetary transactions, automated smart contracts, decentralized identity authentication, and effortless payments.

Development of Organ Targeting Lipid Nanoparticles with Low Immunogenicity and Their Application in the Treatment of Pulmonary Fibrosis

AbstractAs the most advanced non‐viral delivery system, lipid nanoparticles (LNPs) were approved by the FDA, propelling the advancements of gene therapy. However, their clinical applications are hampered by the potential immunogenicity of the lipid components that trigger immune‐related adverse events, like inflammation and allergy. Herein, we formulate various dLNPs with diminished immunogenicity by incorporating dexamethasone (Dex) into liver‐, spleen‐, and lung‐targeting LNPs formulations that exhibit excellent abilities to target specific organs and deliver various types of RNA, such as mRNA and siRNA. In vivo investigations demonstrate unparalleled advantages in safety as compared to conventional LNPs, showing promising potential in the development of RNA therapeutics. Intriguingly, the encapsulation of runt‐related transcription factor‐1 siRNA (siRUNX1) into lung‐targeting dLNPs (dLNPs@siRUNX1) demonstrates remarkable advantages in the treatment of pulmonary fibrosis through the synergy of gene therapy and drug therapy. This research establishes secure and universal platforms for the precise delivery of nucleic acid therapeutics, showcasing promising clinical applications in gene therapy.

Development of Organ Targeting Lipid Nanoparticles with Low Immunogenicity and Their Application in the Treatment of Pulmonary Fibrosis.

As the most advanced non-viral delivery system, lipid nanoparticles (LNPs) were approved by the FDA, propelling the advancements of gene therapy. However, their clinical applications are hampered by the potential immunogenicity of the lipid components that trigger immune-related adverse events, like inflammation and allergy. Herein, we formulate various dLNPs with diminished immunogenicity by incorporating dexamethasone (Dex) into liver-, spleen-, and lung-targeting LNPs formulations that exhibit excellent abilities to target specific organs and deliver various types of RNA, such as mRNA and siRNA. In vivo investigations demonstrate unparalleled advantages in safety as compared to conventional LNPs, showing promising potential in the development of RNA therapeutics. Intriguingly, the encapsulation of runt-related transcription factor-1 siRNA (siRUNX1) into lung-targeting dLNPs (dLNPs@siRUNX1) demonstrates remarkable advantages in the treatment of pulmonary fibrosis through the synergy of gene therapy and drug therapy. This research establishes secure and universal platforms for the precise delivery of nucleic acid therapeutics, showcasing promising clinical applications in gene therapy.

SAGB: self-attention with gate and BiGRU network for intrusion detection

Network traffic intrusion detection technology plays an important role in host and platform security. At present, machine learning and deep learning methods are often used for network traffic intrusion detection. However, the imbalance of relevant data sets will cause the model algorithm to learn the features of the majority categories and ignore the features of the minority categories, which will seriously affect the precision of network intrusion detection models. The number of samples of the attacks is much less than the number of normal samples. The classification performance on unbalanced data sets is poor and can not identify the minority attack samples well. To solve these problems, this paper proposed the resampling mechanism, which used random undersampling for the majority categories samples and K-Smote oversampling for the minority categories samples, so as to generate a more balanced data set and improve the model's detection rate for the minority categories. This paper proposed the Self-Attention with Gate (SAG) and BiGRU network model for intrusion detection on the CICIDS2017 data set, which can fully extract high-dimensional information from data samples and improve the detection rate of network attacks. The Self-Attention with Gate mechanism (SAG) based on the Transformer performed the feature extraction, filtered the irrelevant noise information, then extracted the long-distance dependency temporal sequence features by the BiGRU network, and obtained the classification results through the SoftMax classifier. Compared to the experimental results of other algorithms, such as Random Forest (RF) and BiGRU, it can be found that the overall classification precision for the SAG-BiGRU model in this paper is much higher and also has a good effect on the NSL-KDD data set.

Asthma management in the digital age.

Asthma affects 25 million people in the United States, and its prevalence is increasing. Access to care and adherence to prescribed asthma-treatment programs remain the principal formidable challenges for asthma management. Telemedicine offers substantial opportunities for improved asthma care of patients across the full range of socioeconomic strata. Ever-improving digital tools for asthma assessment and treatment are key components of telemedicine platforms for asthma management. These include a variety of remote patient-monitoring devices, digital inhaler systems, and mobile-health applications that facilitate ongoing assessment and adherence to treatment protocols. Digital tools for monitoring treatment focus on tracking medication use, inhalation technique, and physiological markers such as peak-flow rate and pulse-oximetry. Telemedicine visits allow for elements of assessment via video, approximating or duplicating many aspects of in-person visits, such as evaluating a patient's general appearance, breathing effort, and cough. Challenges remain in ensuring equitable access to these technologies, especially in rural and low-income areas, and in maintaining patient privacy and data security in digital platforms.

A framework for sharing of clinical and genetic data for precision medicine applications.

Precision medicine has the potential to provide more accurate diagnosis, appropriate treatment and timely prevention strategies by considering patients' biological makeup. However, this cannot be realized without integrating clinical and omics data in a data-sharing framework that achieves large sample sizes. Systems that integrate clinical and genetic data from multiple sources are scarce due to their distinct data types, interoperability, security and data ownership issues. Here we present a secure framework that allows immutable storage, querying and analysis of clinical and genetic data using blockchain technology. Our platform allows clinical and genetic data to be harmonized by combining them under a unified framework. It supports combined genotype-phenotype queries and analysis, gives institutions control of their data and provides immutable user access logs, improving transparency into how and when health information is used. We demonstrate the value of our framework for precision medicine by creating genotype-phenotype cohorts and examining relationships within them. We show that combining data across institutions using our secure platform increases statistical power for rare disease analysis. By offering an integrated, secure and decentralized framework, we aim to enhance reproducibility and encourage broader participation from communities and patients in data sharing.

Generative AI in Fintech: Advancing Risk Assessment and Fraud Detection in Digital Payment Technologies

This article explores the transformative potential of artificial intelligence (AI), particularly generative AI, in enhancing risk assessment and fraud detection within digital payment technologies. As financial fraud evolves in sophistication, traditional detection methods often fall short. We propose a novel approach leveraging generative AI to create synthetic data that mimics real-world scenarios, thereby training more comprehensive and adaptive fraud detection models. This article addresses limitations in historical datasets, improves the accuracy of predictive models, and reduces false positives. Furthermore, we demonstrate how this approach enables proactive detection of emerging fraud trends, allowing fintech firms to stay ahead in the ongoing arms race against fraudsters. Our findings suggest that integrating generative AI into fraud detection systems can significantly enhance the security and reliability of digital payment platforms, potentially revolutionizing risk management in the fintech industry. This article contributes to the growing body of knowledge on AI applications in financial security and provides a foundation for future developments in this critical area.

أثر التجارة الإلكترونية على سلوك المستهلكين في قطاع تجارة التجزئة في المملكة العربية السعودية

E-commerce in Saudi Arabia is experiencing rapid growth, driven by technological advancements and government initiatives aligned with Vision 2030. This research aims to understand the impact of e-commerce on consumer behavior in the retail sector by examining factors influencing e-commerce adoption, the challenges consumers face, and how digital transformations affect traditional businesses. The study gathered data from consumer surveys, government reports, and previous studies, using analytical models to understand the relationships between variables and their impact on consumer behavior. Survey results revealed that e-commerce has significantly changed purchasing habits among Saudis, with many preferring online shopping due to convenience and a wide range of options. The key drivers for e-commerce adoption include convenience, time savings, and product variety, while economic factors like competitive pricing play a significant role in attracting consumers. Trust and security in e-commerce platforms are critical, as consumers seek assurance regarding the protection of their financial information and product quality. Consumers face challenges such as product description accuracy, return difficulties, and concerns about data privacy. Digital transformation pressures traditional businesses to adopt digital strategies to remain competitive. Small businesses face difficulties adapting but show potential for innovation through partnerships with major e-commerce platforms. Data shows a remarkable increase in e-commerce revenue in Saudi Arabia from SAR 38.8 billion in 2020 to SAR 204 billion in 2024, with the number of stores rising from 28,676 to 57,000 during the same period. To sustain this growth, companies need to invest in long-term strategies and diversify revenue streams. The Saudi government plays a crucial role in supporting this sector by improving digital infrastructure, enhancing cybersecurity, and supporting startups. By adopting these recommendations, the online shopping experience can be improved, ensuring customer satisfaction and the continued growth of this vital sector.

Investigating Students’ Reading Strategies and Reading Comprehension Through Digital Literacy Environment

Educational technology provides students the chance to learn online and develop independent learners. The availability of digital learning materials is an attempt to realize exactly a widespread learning strategy. This study aimed to investigate the effect of the impact of providing detailed instructions on reading strategies and reading comprehension through a digital literacy environment (DLE) for university students. The research employed a qualitative approach through survey data, and it was conducted in an undergraduate education department at the State University of Makassar, which had 43 students. The findings indicate that students generally acknowledge the effectiveness of digital technologies, such as a private Facebook group and WhatsApp, in providing inclusive and secure platforms for student involvement. These platforms create a comfortable environment for students to share their thoughts. The students were motivated when using digital tools like e-books, interactive reading platforms, and multimedia resources. It can be concluded that discussion groups on Facebook and WhatsApp offer comprehensive, confidential forums for students to improve their digital reading comprehension.

Indonesia's National Defense Resilience during Covid-19: Key Lessons

The COVID-19 pandemic tested national defense systems worldwide, including Indonesia's. This research aims to evaluate the resilience of Indonesia's National Defense System amid the global health crisis, focusing on the effectiveness of the Indonesian National Armed Forces (TNI) in managing logistical challenges, key factors influencing interagency coordination, and the role of technological innovation. Utilizing a qualitative research method based on secondary data, the study analyzes reports, government documents, and academic literature to gather comprehensive insights. Findings reveal that the TNI effectively utilized its disaster response experience to establish robust logistical operations, despite facing supply chain disruptions and geographical challenges. Effective interagency coordination was facilitated by clear roles, communication channels, and collaborative efforts, although bureaucratic hurdles occasionally hindered efficiency. Technological innovations, including digital surveillance, data management, and secure communication platforms, significantly enhanced operational effectiveness and resilience. This research offers a novel perspective by integrating multiple facets of the defense response, highlighting the interplay between logistics, coordination, and technology. The insights gained can inform strategies to strengthen national defense capabilities in future crises.

Security matters: Empowering e-commerce in Sri Lanka through customer insights

In the fast-paced, post-COVID digital world, e-commerce presents promising prospects for significant advancement. However, customers often feel uncertain due to persistent concerns about the robustness of security measures safeguarding e-commerce platforms. The primary objective of our study was to identify factors affecting the security of e-commerce platforms based on the perceptions of Sri Lankan customers. This research was conducted using data collected from Sri Lankan e-commerce users via both online and offline surveys. An ordered probit regression model was utilised, demonstrating that transaction security, privacy, vendor system security, and platform quality positively impact the perceived security of e-commerce. The e-commerce industry in Sri Lanka is expected to see growth and an increased user penetration rate. The findings of this study are anticipated to assist e-commerce business owners and policymakers in addressing critical security issues, namely vulnerabilities in transactional security, low privacy, inadequate system security, and poor e-commerce platform quality. These improvements are expected to build trust and credibility among consumers, maximising e-commerce success.

DemocracyGuard: Blockchain‐based secure voting framework for digital democracy

AbstractOnline voting is gaining traction in contemporary society to reduce costs and boost voter turnout, allowing individuals to cast their ballots from anywhere with an internet connection. This innovation is cautiously met due to the inherent security risks, where a single vulnerability can lead to widespread vote manipulation. Blockchain technology has emerged as a promising solution to address these concerns and create a trustworthy electoral process. Blockchain offers a decentralized network of nodes that enhances transparency, security, and verifiability. Its distributed ledger and non‐repudiation features make it a compelling alternative to traditional electronic voting systems, ensuring the integrity of elections. To further bolster the security of online voting, we propose DemocracyGuard platform on the Ethereum blockchain, which incorporates facial recognition technology to authenticate voters. By leveraging these advancements, DemocracyGuard aims to provide a secure and resilient platform for online voting, paving the way for its broader adoption and revolutionizing the electoral landscape.

Anomaly detection combining bidirectional gated recurrent unit and autoencoder in the context of e-commerce

E-commerce platforms store a large amount of user personal information, transaction data, and financial information, which have extremely high value for hackers and criminals. Therefore, protecting the security of e-commerce platforms is particularly important, and intrusion detection is a technical means used to discover and respond to possible security threats and attacks. But with the development of Internet technology, there are more and more types of intrusion attacks and more sophisticated means. Traditional intrusion detection systems are difficult to cope with. This study proposes an anomaly detection model based on bidirectional gated loop units and autoencoders. The model learns HTTP text data, trains the model, and uses bidirectional gated loop units to convert text sequences from characters to numbers. The experimental results show that when the training set size is 1000, the false alarm rates of Analytic Hierarchy Process, Support Vector Machine, Long Short Term Recurrent Memory Network, and Improved end-to-end algorithm models are 0.30, 0.27, 0.23, and 0.10, respectively. The loss function values are 0.35, 0.28, 0.17, and 0.13, respectively. The F1 values are 0.78, 0.88, 0.91, and 0.99, and the accuracy rates are 0.88, 0.91, 0.95, and 0.99, respectively. The research results indicate that the proposed method model has excellent performance.

Comparative Security and Compliance Analysis of Serverless Computing Platforms: AWS Lambda, Azure Functions, and Google Cloud Functions

Serverless computing has revolutionized cloud services by abstracting infrastructure management, enabling developers to focus on application logic. This research examines the security and compliance features of three major serverless platforms: AWS Lambda, Azure Functions, and Google Cloud Functions. By evaluating authentication mechanisms, data encryption practices, vulnerability management, and compliance certifications, we aim to provide a comparative analysis that informs businesses and developers on the most secure and compliant platform for their needs. Keywords: Serverless Computing, Security, Compliance, AWS Lambda, Azure Functions, Google Cloud Functions, Cloud Security Alliance, Cloud Controls Matrix

Customized Flexible Osteoid Electrode for Traumatic Brain Injury Induced Posttraumatic Epilepsy Monitoring

AbstractTraumatic brain injury (TBI)‐induced post‐traumatic epilepsy (PTE) is a serious brain disease that causes the loss of motor functions, cognitive impairments, and even death. Real‐time and long‐term electrophysiological signal monitoring has significant implication for the treatment of patient with TBI. Brain–computer interfaces (BCIs) utilizing flexible electrodes enable accurate epilepsy identification by electrocorticography (ECoG) signal monitoring. In this study, a customized flexible osteoid electrode is developed, which contributes to demands and personal differentiation among patients with TBI. The end product of osteoid electrode after full osseointegration will be a stable and firmly integrated structure within the bone tissue, providing a secure platform for continuous electrophysiological signal monitoring. The flexibility and conductivity of the osteoid electrode enables high‐quality and long‐term epilepsy monitoring. Biocompatibility of the osteoid electrode is also demonstrated by in vitro cell experiments. Simultaneously, the porous osteoid electrode also exhibits osseointegration ability based on potential long‐term ECoG signal monitoring, which suggests its potential for the comprehensive treatment of TBI patients. This work not only introduces a potential therapeutic scaffold for TBI‐related diseases, but also provides promising techniques for clinical ECoG monitoring in patients with TBI.

A LOG MANAGEMENT SYSTEM (LMS) FOR ADMINISTRATORS, EMPLOYEES, STUDENTS, AND VISITORS

This thesis introduces a QR Code-based Log Management System (LMS) for St. Peter's College, replacing manual logging with a web-based platform. The LMS enables users to scan QR Codes, eliminating log books and improving accuracy. It automates administrative tasks, recording timestamps, user information, vaccination status, and enrollment status. Utilizing QR Code technology sets this study apart from similar systems that use RFID. QR Codes are costeffective and convenient for identification. The LMS integrates QR Codes into identification cards for administrators, employees, and students, while visitors can generate QR Codes through the web application. Additional features include uploading vaccination cards and Certificates of Registration (COR) for verification. Developed using the Django framework, the LMS provides a secure and rapid development platform accessible via standard web browsers. Its implementation improves data collection accuracy, facilitates contact tracing, and enhances safety through SMS notifications. This thesis contributes to the digital transformation of administrative processes in educational institutions, streamlining operations and improving data management. The researchers employed the Agile Model for development, allowing for continuous feedback and adaptation. This approach ensured the LMS met St. Peter's College's specific requirements. To assess usability, the Technology Acceptance Model (TAM) was used, evaluating factors like ease of use and user satisfaction. The combination of the Agile Model and TAM offers a comprehensive approach to designing and implementing the QR Code-based Log Management System, aligning it with the needs and preferences of St. Peter's College.