Web Application Pages Research Articles

FRONT-END ФРЕЙМВОРК ДЛЯ ПОБУДОВИ ЗАСТОСУНКІВ З АДАПТИВНИМ ГРАФІЧНИМ ІНТЕРФЕЙСОМ ЗАСОБАМИ МАШИННОГО НАВЧАННЯ

The article examines approaches to developing a front-end framework for creating web applications with an adaptive graphical interface that dynamically adjusts to the individual needs of users through machine learning algorithms. The relevance of the problem lies in the need to develop interfaces capable of simultaneously meeting the needs of different demographic groups, which requires flexibility in customizing the user experience (UX) and user interface (UI) of modern websites. Traditional interface design methods do not always account for the specific needs of each user, which reduces the effectiveness of interaction with the site. The article proposes an approach that utilizes reinforcement learning algorithms to analyze user interaction with the interface and automatically adapt the interface based on behavioral data. This enhances the accuracy of interface personalization and improves the overall user experience. The goal of the work is to develop a tool that enables the automated restructuring of the graphical interface of web applications based on individual user needs to improve their user experience. The research develops algorithms to optimize user interaction with web application pages and improve interface efficiency. The research results demonstrate the framework's ability to dynamically respond to user behavior, assess their level of interaction, and make informed decisions regarding interface parameter adaptation, which in turn helps developers to reduce amount of work needed to implement personalized interface by eliminating the need to manually develop website variants. Using this approach the estimated code base reduction is 40-50%. Keywords: adaptive interface, front-end, machine learning, user experience, web design.

Research into Occurrence of Insecurely-Serialized Objects in Client-Side Code of Web-Applications

This paper studies the occurrence of insecure deserialization in communication between client-side code and the server-side of a web application. Special attention was paid to serialized objects sent from JavaScript client-side code. Specific patterns of using serialized objects within the client-side JavaScript code were identified and unique classes were formulated, whose main goal is to facilitate manual and automatic analysis of web applications. A tool that detects a serialized object in the client-side code of a web page has been designed and implemented. This tool is capable of finding encoded serialized objects as well as serialized objects encoded using several sequentially applied encodings. For found samples of serialized objects, the tool determines the context in which the found object appears on the page. For objects inside JavaScript code, the tool identifies the previously mentioned classes by mapping the vertices of the abstract syntax tree (AST) of the code. Web application endpoints were checked for whether programming objects were deserialized on the server side, after obtaining the results of the study. As a result of this check, previously unknown vulnerabilities were found, which were reported to the developers of this software. One of them was identified as CVE-2022-24108. Based on the results of this research, a method was proposed to facilitate both manual and automated searches for vulnerabilities of the "Deserialization of untrusted data". The proposed algorithm was tested on more than 50,000 web application pages from the Alexa Top 1M list, as well as on 20,000 web application pages from Bug Bounty programs.

SMARTBELL SEBAGAI APLIKASI IoT PADA SISTEM KEAMANAN RUMAH

In this paper, a smart bell system is designed and realized. The system uses a passive-infrared sensor to detect motion and activate the system to logged detection time to database, sent notification, and commanda webcam to take pictures and record video. An Arduino microcontroller is used to process datathat will be presented on web pages which are then converted into Progressive Web Applications. Node.js is used as the platform to creare a server, and MongoDB is employed as database for time detection. For communication system protocol between the database and web application pages the REST-API method isused. During the detection process, the system will send a notification using webpush with the Firebase Cloud Messanging framework. The system was tested to detect, record videos, create GIFs, write to databases, and send notifications into web applications. The experiments shows a success of 81.25% for displaying videosand of 93.75% for displaying GIFs.

Identifying similar pages in Web applications using a competitive clustering algorithm

AbstractWe present an approach based on Winner Takes All (WTA), a competitive clustering algorithm, to support the comprehension of static and dynamic Web applications during Web application reengineering. This approach adopts a process that first computes the distance between Web pages and then identifies and groups similar pages using the considered clustering algorithm. We present an instance of application of the clustering process to identify similar pages at the structural level. The page structure is encoded into a string of HTML tags and then the distance between Web pages at the structural level is computed using the Levenshtein string edit distance algorithm. A prototype to automate the clustering process has been implemented that can be extended to other instances of the process, such as the identification of groups of similar pages at content level. The approach and the tool have been evaluated in two case studies. The results have shown that the WTA clustering algorithm suggests heuristics to easily identify the best partition of Web pages into clusters among the possible partitions. Copyright © 2007 John Wiley & Sons, Ltd.