Web Page Content Research Articles

Unlocking Potential: Analyzing the Content, Style, Structure, and Interactivity of Mesonets as Operational Dashboards

Abstract Emergency managers need data and information to make life-saving decisions on behalf of the public. Operational dashboards, if designed appropriately, can provide this information in a central location and reduce cognitive demands during decision-making. Mesonet websites can serve as a type of operational dashboard that has the potential to provide the meteorological data necessary for emergency managers to make decisions. In this study, we use quantitative content analysis to examine the content, style, structure, and interactivity of 18 Mesonet websites from across the contiguous United States. We find that Mesonet websites vary in the type and amount of content they include. For website style, we find that Mesonet websites primarily present their content with maps and data filters. We find that the structure of the website content was consistent across websites. Finally, we find that most Mesonet websites lacked interactivity, or visual feedback, which inhibits ease of use. We discuss extensions for future work. Significance Statement This study captures the content, style, structure, and interactivity of Mesonet websites when they are used as operational dashboards to support weather-related decision-making. Weather dashboards have the potential to support decision-makers of all types during severe weather events by providing all critical information in one place. With this streamlined approach, decision-making is made more efficient, as gathering information from multiple sources is no longer necessary. Mesonet dashboards provide a valuable context to analyze the content, style, and structure of webpages that can be useful for users. Through this work, we hope to identify the design principles and trends present in these dashboards, providing a basis for future research and efforts to improve design, user experience, and accessibility.

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.

Early Detection of Phishing Sites with Enhanced Neural Network Models

Phishing is a digital crime committed with the aim of obtaining personal data by creating a link or website that resembles the original. This form of cyber attack is caused by a notification in a text message, email, or phone call. A common anti-phishing countermeasure technique is to perform early detection of potentially phishing sites, primarily according to the source code features, which are required to traverse web page content, as well as third parties that slow down the process of clarifying phishing URLs. Although the latest technology has long been used in phishing early detection, there is still a need for manual feature engineering that is important and reliable enough to detect emerging phishing offenses. One of these involves training a neural network (NN) using a dataset of known phishing URLs and legitimate URLs. The research was conducted using 200 data, Data were separated into training and testing categories. Training was done using 100 and 120 data. Training results on 100 data and 160 data had lower iterations and errors on the tanh activation function compared to the logistic activation function. The number of iterations that occur in logistic activation is as many as 400 iterations, while when using the tanh activation function only 175 iterations are needed.

The Phisherman’s Gambit

Phishing remains a pervasive threat in the digital landscape, posing significant risks to individuals and organizations alike. In response, this paper presents a sophisticated approach to phishing website detection utilizing machine learning methodologies. Through the amalgamation of diverse features extracted from URLs, domain attributes, and webpage content, a robust classification framework is constructed. Employing an array of supervised learning algorithms, including decision trees, support vector machines, and neural networks, our proposed system demonstrates its efficacy in accurately discerning phishing websites with notable precision and recall rates. Moreover, feature engineering and selection techniques are implemented to optimize model performance and computational efficiency. Experimental evaluations conducted on comprehensive datasets validate the effectiveness of the proposed approach in proactively identifying and mitigating phishing threats, thereby fortifying cybersecurity measures for users and organizations in the digital realm.

Racial Diversity of Patient Population Represented on United States Plastic Surgeons' Webpages.

Current literature demonstrates a lack of racial diversity in plastic surgery media. However, to our knowledge, no study has yet examined the racial diversity of Webpage content as if from a patient-search perspective. The objective of this study is to determine if there is a racial discrepancy between the US Census, American Society of Plastic Surgeons (ASPS) statistics, and the media featuring implied patients on US plastic surgeons' Webpages from a patient-focused approach. A Google search was completed using the term "(state) plastic surgeon." The first 10 relevant Web sites were collected for each state, and homepages were analyzed. In line with previous studies, the implied patients in media were classified into 1 of 6 skin tone categories: I, ivory; II, beige; III, light brown; IV, olive; V, brown; and VI, dark brown. These correlate to Fitzpatrick phototypes; however, the Fitzpatrick scale measures skin's response to UV exposure. Skin tone was used as a guide to measure racial representation in the media, with the caveat that skin tone does not absolutely correlate to racial identity. Categories I-III were further classified as "white" and IV-VI as "nonwhite." These data were compared with the 2020 ASPS demographics report and US Census. Four thousand eighty individuals were analyzed from 504 Webpages, the majority of which were those of private practice physicians. A total of 91.62% of individuals were classified as "white" and 8.38% "nonwhite." The distribution by category was as follows: I = 265, II = 847, III = 2626, IV = 266, V = 71, and VI = 5. Using χ2 analyses, a statistically significant difference was found between the racial representation within this sample and that of the 2020 US Census nationally (P < 0.001), regionally (P < 0.001), and subregionally (P < 0.001); the 2020 ASPS Cosmetic Summary Data (P < 0.001); and the 2020 ASPS Reconstructive Summary Data (P < 0.001). This study highlights the significant difference between racial representation on plastic surgeons' Webpages and the demographics of patients they serve. Further analyses should identify the impact of these representational disparities on patient care and clinical outcomes, as well as examine how best to measure racial diversity and disparities in patient-oriented media.

Developing Decision Tree for Measuring Impact of Errors in Web Page Content using Data Mining Techniques

Developing Decision Tree for Measuring Impact of Errors in Web Page Content using Data Mining Techniques

Web Page Ranking Based on Text Content and Link Information Using Data Mining Techniques

Thanks to the rapid expansion of the Internet, anyone can now access a vast array of information online. However, as the volume of web content continues to grow exponentially, search engines face challenges in delivering relevant results. Early search engines primarily relied on the words or phrases found within web pages to index and rank them. While this approach had its merits, it often resulted in irrelevant or inaccurate results. To address this issue, more advanced search engines began incorporating the hyperlink structures of web pages to help determine their relevance. While this method improved retrieval accuracy to some extent, it still had limitations, as it did not consider the actual content of web pages. The objective of the work is to enhance Web Information Retrieval methods by leveraging three key components: text content analysis, link analysis, and log file analysis. By integrating insights from these multiple data sources, the goal is to achieve a more accurate and effective ranking of relevant web pages in the retrieved document set, ultimately enhancing the user experience and delivering more precise search results the proposed system was tested with both multi-word and single-word queries, and the results were evaluated using metrics such as relative recall, precision, and F-measure. When compared to Google’s PageRank algorithm, the proposed system demonstrated superior performance, achieving an 81% mean average precision, 56% average relative recall, and a 66% F-measure.

Webpage Metadata Extration Using Machine Learning Techiniques

Abstract: This Python script defines a Flask web application enabling users to input a URL. The application fetches the webpage content and utilizes TF-IDF (Term Frequency-Inverse Document Frequency) analysis to extract information like the title, description, and top keywords. The / route renders an HTML template (index.html) for user input, while the /extract route handles a POST request, fetching the webpage content, extracting relevant information using TF-IDF analysis, and rendering the results in another HTML template (result.html). The TF-IDF process involves tokenizing the text, eliminating stopwords, and calculating TF-IDF scores for each term. The top 10 keywords are then extracted based on their TF-IDF scores. The script also incorporates error handling for cases where the webpage cannot be fetched or an exception occurs during the process.

Automatic Topic-Based Web Page Classification Using Deep Learning

The internet is frequently surfed by people by using smartphones, laptops, or computers in order to search information online in the web. The increase of information in the web has made the web pages grow day by day. The automatic topic-based web page classification is used to manage the excessive amount of web pages by classifying them to different categories based on the web page content. Different machine learning algorithms have been employed as web page classifiers to categorise the web pages. However, there is lack of study that review classification of web pages using deep learning. In this study, the automatic topic-based classification of web pages utilising deep learning that has been proposed by many key researchers are reviewed. The relevant research papers are selected from reputable research databases. The review process looked at the dataset, features, algorithm, pre-processing used in classification of web pages, document representation technique and performance of the web page classification model. The document representation technique used to represent the web page features is an important aspect in the classification of web pages as it affects the performance of the web page classification model. The integral web page feature is the textual content. Based on the review, it was found that the image based web page classification showed higher performance compared to the text based web page classification. Due to lack of matrix representation that can effectively handle long web page text content, a new document representation technique which is word cloud image can be used to visualize the words that have been extracted from the text content web page.

Detecting Deceptive Dark-Pattern Web Advertisements for Blind Screen-Reader Users.

Advertisements have become commonplace on modern websites. While ads are typically designed for visual consumption, it is unclear how they affect blind users who interact with the ads using a screen reader. Existing research studies on non-visual web interaction predominantly focus on general web browsing; the specific impact of extraneous ad content on blind users' experience remains largely unexplored. To fill this gap, we conducted an interview study with 18 blind participants; we found that blind users are often deceived by ads that contextually blend in with the surrounding web page content. While ad blockers can address this problem via a blanket filtering operation, many websites are increasingly denying access if an ad blocker is active. Moreover, ad blockers often do not filter out internal ads injected by the websites themselves. Therefore, we devised an algorithm to automatically identify contextually deceptive ads on a web page. Specifically, we built a detection model that leverages a multi-modal combination of handcrafted and automatically extracted features to determine if a particular ad is contextually deceptive. Evaluations of the model on a representative test dataset and 'in-the-wild' random websites yielded F1 scores of 0.86 and 0.88, respectively.

Safeguard confidential web information from malicious browser extension using Encryption and Isolation techniques

Web browsers have become an integral part of our daily lives, granting us access to vast information and services. However, this convenience significantly risks personal information and data security. One common source of this risk is browser extensions, which users often employ to add new features to their browsers. Unfortunately, these extensions can also pose a security threat, as malicious ones may access and steal sensitive information such as passwords, credit card details, and personal data. The vulnerability of web browsers to malicious extensions is a significant challenge that effectively tackles through robust defence mechanisms. To address this concern, Secure Vault – API is proposed and designed to safeguard confidential web page content from malicious extensions. The Web Crypto API provides cryptographic functions that protect data during transmission and storage. The Secure Vault encompasses a Sentinel extension responsible for parsing the web page’s Document Object Model (DOM) content and querying for all “vault” elements. The extension then verifies that the DOM content has not been tampered with by any malicious extension by calculating the SHA512 hash value of the concatenated vault elements as a string, with no whitespace between them. With its encryption, hashing, and isolation techniques, the Secure Vault effectively protects confidential web page content from malicious extensions. It provides a secure environment for storing and processing sensitive data, reducing the risk of data breaches caused by malicious extensions. The proposed approach offers significant advantages over existing strategies in terms of protecting confidential web page content from malicious extensions. This not only improves the efficiency and effectiveness of the browser extensions but also ensures compatibility, interoperability and performance across different web browsers with respect to the load time of HTML elements. Users can browse the web and carry out sensitive transactions with peace of mind, knowing their data is safeguarded against theft or manipulation by malicious extensions.

A Convolutional Neural Network (CNN) Classification Model for Web Page: A Tool for Improving Web Page Category Detection Accuracy

Game and Online Video Streaming are the most viewed web pages. Users who spend too much time on these types of web pages may suffer from internet addiction. Access to Game and Online Video Streaming web pages should be restricted to combat internet addiction. A tool is required to recognise the category of web pages based on the text content of the web pages. Due to the unavailability of a matrix representation that can handle long web page text content, this study employs a document representation known as word cloud image to visualise the words extracted from the text content web page after data pre-processing. The most popular words are shown in large size and appear in the centre of the word cloud image. The most common words are the words that appear frequently in the text content web page and are related to describing what the web page content is about. The Convolutional Neural Network (CNN) recognises the pattern of words presented in the core portions of the word cloud image to categorise the category to which the web page belongs. The proposed model for web page classification has been compared with the other web page classification models. It shows the good result that achieved an accuracy of 85.6%. It can be used as a tool that helps to make identifying the category of web pages more accurate

Structural Analysis of URL For Malicious URL Detection Using Machine Learning

Malicious websites are intentionally created websites that aid online criminals in carrying out illicit actions. They commit crimes like installing malware on the victim's computer, stealing private data from the victim's system, and exposing the victim online. Malicious codes can also be found on legitimate websites. Therefore, locating such a website in cyberspace is a difficult operation that demands the utilization of an automated detection tool. Currently, machine learning/deep learning technologies are employed to detect such malicious websites. However, the problem persists since the attack vector is constantly changing. Most research solutions use a limited number of URL lexical features, DNS information, global ranking information, and webpage content features. Combining several derived features involves computation time and security risk. Additionally, the dataset's minimal features don't maximize its potential. This paper exclusively uses URLs to address this problem and blends linguistic and vectorized URL features. Complete potential of the URL is utilized through vectorization. Six machine learning algorithms are examined. The results indicate that the proposed approach performs better for the count vectorizer with random forest algorithm

Does banner advertising still capture attention? An eye-tracking study

Purpose The purpose of this study is to investigate how much visual attention is given to banner ads embedded in Web page content dependent on whether the user’s task is goal- or not goal-oriented, as well as the interplay between attention, banner location, banner click and banner recognition. Design/methodology/approach The authors used a within-subjects design where 100 participants performed two tasks – reading a news and finding where to click next – on a Web page containing three banner ads embedded into the website content. The authors gathered behavioral and eye-tracking data. Findings Consumers disregard banner ads when they are performing a focused task (reading news). Visual attention paid to the banners while reading – but not while free browsing – and banner location do not impact ad clicking. In addition, it is not necessary to pay full attention to a banner ad to be able to recognize it afterward. Practical implications The strategy of embedding banners in the main content of a Web page leads to higher visual attention when consumers are browsing a Web page compared to a focused task (e.g. reading). It also increases ad recognition over time compared to benchmark levels for ads placed in traditional positions. Originality/value Previous studies mainly assessed effectiveness of banners located at the top or lateral of a Web page. The authors used eye tracking as an objective measure of visual attention to banner ads embedded in Web page content and behavioral metrics to assess ad interest and measured ad recognition over time.

Development of an algorithm for obtaining data from thematic internet resources

With the rapid development of the Internet, users are actively sharing their personal data and other information on many social networks. Information on the Internet should be analyzed to make sure that it is reliable and does not pose a threat to the public. Based on this, there is a need to collect, monitor and analyze this information. Data collection is a complex task, depending on the structure of each web page. Since not all resources allow you to collect information, you have to use many methods. The proposed article shows effective ways of using syntactic analysis to obtain information. The method of semantic analysis (parsing) of the contents of web pages is explained using a program written in Python based on the BeatifulSoup library. In addition, the focus is on methods of collecting information through other APIs, using tools to emulate user behavior in the browser. An algorithm for extracting information from thematic Internet resources using the BeatifulSoup + Requests library is presented. As a result, information was obtained from Englishand Russian-speaking hacker and carding forums.

The Early Impact of GDPR Compliance on Display Advertising: The Case of an Ad Publisher

The European Union's General Data Protection Regulation (GDPR), with its explicit consent requirement, may restrict the use of personal data and shake the foundations of online advertising. The ad industry has predicted drastic loss of revenue from GDPR compliance and has been seeking alternative ways of targeting. Taking advantage of an event created by an ad publisher's request for explicit consent from users with European Union IP addresses, the authors find that for a publisher that uses a pay-per-click model, has the capacity to leverage both user behavior and web page content information for advertising, and observes high consent rates, GDPR compliance leads to modest negative effects on ad performance, bid prices, and ad revenue. The changes in ad metrics can be explained by temporal variations in consent rates. The impact is most pronounced for travel and financial services advertisers and least pronounced for retail and consumer packaged goods advertisers. The authors further find that web page context can compensate for the loss of access to users’ personal data, as the GDPR's negative impact is less pronounced when ads are posted on web pages presenting relevant content. The results suggest that publishers and advertisers should leverage targeting based on web page content after the GDPR’s rollout.

Feature Selection toEnhance Phishing Website Detection Based On URL Using Machine Learning Techniques

The detection of phishing websites based on machine learning has gained much attention due to its ability to detect newly generated phishing URLs. To detect phishing websites, most techniques combine URLs, web page content, and external features. However, the content of the web page and external features are time-consuming, require large computing power, and are not suitable for resource-constrained devices. To overcome this problem, this study applies feature selection techniques based on the URL to improve the detection process. The methodology for this study consists of seven stages, including data preparation, preprocessing, splitting the dataset into training and validation, feature selection, 10-fold cross-validation, validating the model, and finally performance evaluation. Two public datasets were used to validate the method. TreeSHAP and Information Gain were used to rank features and select the top 10, 15, and 20. These features are fed into three machine learning classifiers which are Naïve Bayes, Random Forest, and XGBoost. Their performance is evaluated based on accuracy, precision, and recall. As a result, the features ranked by TreeSHAP contributed most to improving detection accuracy. The highest accuracy of 98.59 percent was achieved by XGBoost for the first dataset with 15 features. For the second dataset, the highest accuracy is 90.21 percent using 20 features and Random Forest. As for Naïve Bayes, the highest accuracy recorded is 98.49 percent using the first dataset.

The presentation of gut microbiome-based personalized nutrition on the internet: simple and accessible, complex and inaccessible

The gut microbiome is related to health and wellbeing, although the precise nature of the relationship and the involvement of and interaction with other factors is not fully understood. In this context, private companies are providing gut microbiome-based personalized nutrition services on the internet. Framed by social representation theory, we conducted an analysis of the websites of 27 companies offering direct-to-consumer personalized nutrition based on the microbiome, to understand how they communicate to prospective consumers. We found that through imagery, metaphor, and personification, companies simultaneously position the gut microbiome, and gut microbiome-based personalized nutrition, as simple and accessible and complex and inaccessible. Highly medicalized content in the main web pages is negated in often peripheral disclaimer sections.

Appraisal of online health information on intravenous iron in Canada: Are patients well-informed?

Evaluate quality, content, and readability of the most-accessed webpages on intravenous iron in Canada.

A Phishing-Attack-Detection Model Using Natural Language Processing and Deep Learning

Phishing is a type of cyber-attack that aims to deceive users, usually using fraudulent web pages that appear legitimate. Currently, one of the most-common ways to detect these phishing pages according to their content is by entering words non-sequentially into Deep Learning (DL) algorithms, i.e., regardless of the order in which they have entered the algorithms. However, this approach causes the intrinsic richness of the relationship between words to be lost. In the field of cyber-security, the innovation of this study is to propose a model that detects phishing attacks based on the text of suspicious web pages and not on URL addresses, using Natural Language Processing (NLP) and DL algorithms. We used the Keras Embedding Layer with Global Vectors for Word Representation (GloVe) to exploit the web page content’s semantic and syntactic features. We first performed an analysis using NLP and Word Embedding, and then, these data were introduced into a DL algorithm. In addition, to assess which DL algorithm works best, we evaluated four alternative algorithms: Long Short-Term Memory (LSTM), Bidirectional LSTM (BiLSTM), Gated Recurrent Unit (GRU), and Bidirectional GRU (BiGRU). As a result, it can be concluded that the proposed model is promising because the mean accuracy achieved by each of the four DL algorithms was at least 96.7%, while the best performer was BiGRU with 97.39%.