Showing papers on "Web application published in 2026"
8 Jan 2026
TL;DR: CryptoSQLShield, a dual-layer defense architecture, integrates cryptographic input sanitization and real-time query analysis to prevent and detect SQL injection attacks, offering flexible encryption strategies and improved recall for detecting obfuscated payloads.
Abstract: SQL injection (SQLi) represents a significant threat to the integrity and security of web applications by exploiting vulnerabilities within database systems, thereby allowing unauthorized access and manipulation of sensitive information. Current mitigation strategies, including input validation, parameterized queries, web application firewalls (WAFs), and machine learning detection algorithms, often fall short against sophisticated attacks that employ obfuscation and fragmentation. This study introduces CryptoSQLShield, an innovative dual-layer defense architecture designed to enhance resilience against SQLi attacks. The framework integrates cryptographic input sanitization with real-time query analysis, facilitating a prevention module using customizable user-defined encryption/decryption (UDF) mechanisms to decouple user input from query syntax. Concurrently, a detection module applies template-based validation and anomaly scoring to reconstructed queries before execution. Unlike traditional solutions that rely on static cryptographic protocols, CryptoSQLShield offers flexible encryption strategies, adaptable for various contexts, including controlled environments, research applications, and educational settings. Empirical evaluations conducted on standard SQLi testing frameworks reveal a substantial decline in exploitation success rates, alongside improved recall for detecting obfuscated payloads, substantiating the framework's efficacy in achieving a favorable balance among attack resilience, false-positive reduction, and operational efficiency.
9 Jan 2026
TL;DR: This research presents a MERN-stack Job Portal Web Application addressing traditional recruitment limitations with a centralized, interactive platform offering secure authentication, job posting, and profile management, demonstrating low latency, stable performance, and secure data handling.
Abstract: This research paper presents the design and implementation of a full-stack Job Portal Web Application developed using the MERN stack—MongoDB, Express.js, React.js, and Node.js. The study addresses the limitations of traditional recruitment methods such as manual resume screening, newspaper advertisements, and fragmented digital job listings. The proposed system provides a centralized, interactive, and scalable platform that connects job seekers with employers through features such as secure authentication, job posting, job searching with filters, profile and resume management, and application tracking. The paper explains the system architecture, functional modules, data flow model, technology stack, and implementation methodology in detail. Performance and usability evaluations demonstrate that the application offers low latency, stable performance under load, and an intuitive user interface. Security considerations, including encrypted credentials and controlled user permissions, ensure safe data handling. The platform supports cloud deployment and horizontal scalability, making it suitable for campus placements, corporate hiring, and freelance opportunities. Overall, the research highlights how a MERN-based recruitment system can simplify hiring workflows, enhance accessibility, and offer a modern technological solution to recruitment challenges. The paper concludes with potential future enhancements such as AI-powered job recommendations, resume parsing, interview scheduling, and mobile app integration.
TL;DR: This paper presents ReactSmart, a machine learning framework that optimizes React-based web application performance by proactively loading components based on predicted user navigation paths, reducing wait times and improving responsiveness by 47.3% and 52.1% respectively.
Abstract: Modern React-based web applications face significant performance challenges as application complexity increases, resulting in prolonged component loading times and degraded user experience. Search engines emphasis on Core Web Vitals metrics further necessitates performance optimization for maintaining web visibility. This paper presents ReactSmart, a machine learning framework that implements adaptive resource management for React component loading through predictive user behavior analysis. ReactSmart employs real-time decision making algorithms that analyze four contextual dimensions: user interaction patterns, application state, network conditions, and device capabilities. The system utilizes these inputs to proactively load components based on predicted user navigation paths, thereby reducing wait times and improving application responsiveness. Our experimental evaluation compares ReactSmart against React Suspense and Guess.js using 10,000 simulated user sessions across multiple application archetypes. Results demonstrate that ReactSmart achieves a 47.3% reduction in initial loading times, 52.1% improvement in Time-to-Interactive (TTI) metrics, and maintains 89.7% prediction accuracy for component usage forecasting within 30-second intervals. The machine learning model incorporates supervised learning techniques trained on user interaction datasets to optimize component prefetching decisions. This research contributes to bridging the gap between theoretical machine learning approaches and practical web performance engineering by providing an open-source framework that enables developers to implement intelligent component loading strategies without requiring deep machine learning expertise. The ReactSmart software and evaluation framework are freely available as open source at https://github.com/sameermankotia/ReactSmart-performance-analysis/tree/master.