Uniform Resource Research Articles

Sharing without clicking on news in social media.

Social media have enabled laypersons to disseminate, at scale, links to news and public affairs information. Many individuals share such links without first reading the linked information. Here we analysed over 35 million public Facebook posts with uniform resource locators shared between 2017 and 2020, and discovered that such 'shares without clicks' (SwoCs) constitute around 75% of forwarded links. Extreme and user-aligned political content received more SwoCs, with partisans engaging in it more than politically neutral users. In addition, analyses with 2,969 false uniform resource locators revealed higher shares and, hence, SwoCs by conservatives (76.94%) than liberals (14.25%), probably because, in our dataset, the vast majority (76-82%) of them originated from conservative news domains. Findings suggest that the virality of political content on social media (including misinformation) is driven by superficial processing of headlines and blurbs rather than systematic processing of core content, which has design implications for promoting deliberate discourse in the online public sphere.

Evolving Methods and Application of a 3D Printed Model in the Current Deep Inferior Epigastric Perforator Flap Elevation.

The choice of the deep inferior epigastric perforator (DIEP) flap for breast reconstruction has increased. Three-dimensional (3D) models of DIEP flaps have been introduced with advances in 3D technology to assist the tedious intramuscular dissection of the pedicle. This study presents the process of creating an ideal 3D model for a DIEP flap. Collaboration among various professionals, including surgeons, radiologists, and 3D-printing model makers, is essential for manufacturing suitable 3D-printing DIEP models. Our team facilitated the collaboration process by sharing the virtual 3D model via a uniform resource locator (URL) using an online platform accessible on mobile devices. During collaboration in 3D DIEP model-making, we continuously upgraded the model to a better version. The model was modified according to the surgeon's preference from a 1:1 ratio size to a handheld size. Additionally, we achieved significant advancements over conventional methods, including improved visualization of detailed vessel pathways with separation of arteries and veins, tissue transparency, and superficial inferior epigastric vein (SIEV). Through 3D model-making collaborations, our team successfully developed a 3D DIEP model with translucency and visualized arteries and veins separately with accentuation of the perforators, including SIEV tracing, which is more accurate. This ideal 3D model for the DIEP flap enables the selection of a perforator vessel that is safe and easy to dissect to lower complications and procedure time and will be of great help to less experienced surgeons.

Illegitimate Websites Detection Using Deep Learning Framework

Phishing is a crime involving robbery of confidential user data. The phishing websites are aimed at individuals, businesses, and cloud storage and government websites. Hardware- based anti-phishing methods are generally used, but software- based approaches are favored because of costs and operational factors. There is no solution to the problem such as zero-day phishing attacks from current phishing detection approaches. A three-phase attack detection called the Phishing Attack Detector based on Web Crawler was proposed to resolve these problems and precisely detect phishing incidences using recurrent neural network. It includes the input features Web traffic, web content and Uniform Resource Locator (URL) based on the classification of phishing and non-phishing pages

Bridging IoT Protocols with the Web of Things: A Path to Enhanced Interoperability

Nowadays, the Internet of Things (IoT) is reshaping numerous application domains. Amidst the different communication protocols currently available (MQTT, CoAP, AMQP, DPWS, etc.) and the abundance of management platforms, the IoT domain has stumbled into vertical silos of proprietary systems hindering interoperability. Within this intricate and fragmented ecosystem, the need for an intelligent and scalable architecture promoting interoperability becomes imperative to maximize the potential of IoT. This paper introduces a system designed to enhance the convergence of IoT protocols (e.g., MQTT, CoAP, AMQP, etc.) with the Web of Things (WoT) paradigm. Our proposed gateway-based solution integrates two systems: Stack4Things (S4T) and Data eXchange Mediator Synthesizer (DeXMS). The role of the latter is to adapt, at the gateway level, the IoT protocols and expose the resources/functionalities of the IoT devices as RESTful APIs using HTTP. Meanwhile, the former (i.e., S4T), leveraging its Dynamic DNS system, ensures that these RESTful resources (exposed at the gateway) are accessible over the Web using publicly routable Uniform Resource Locators (URLs) even when the gateway is deployed behind networking middle boxes (e.g., NATs and firewalls).

DaE2: Unmasking malicious URLs by leveraging diverse and efficient ensemble machine learning for online security

Over 5.44 billion people now use the Internet, making it a vital part of daily life, enabling communication, e-commerce, education, and more. However, this huge Internet connectivity also raises concerns about online privacy and security, particularly with the rise of malicious Uniform Resource Locators (URLs). Recently, conventional ensemble models have attracted attention due to their notable benefits of reducing the variance in models, enhancing predictive performance, improving prediction accuracy, and demonstrating high generalization potential. But, its application in addressing the challenge of malicious URLs is still an open problem. These URLs often hide behind static links in emails or web pages, posing a threat to individuals and organizations. Despite blacklisting services, many harmful sites evade detection due to inadequate scrutiny or recent creation. Hence, to improve URL detection, a Diverse and Efficient Ensemble (DaE2) machine learning algorithm was developed using four ensemble models, that is, AdaBoost, Bagging, Stacking, and Voting to classify URLs. After preprocessing, the experimental result shown that all models achieved over 80 % accuracy, with AdaBoost reaching 98.5 % and Stacking offering the fastest runtime. AdaBoost and Bagging also delivered strong performance, with F1 scores of 0.980 and 0.976, respectively.

Enhancing Online Security: A Novel Machine Learning Framework for Robust Detection of Known and Unknown Malicious URLs

The rapid expansion of the internet has led to a corresponding surge in malicious online activities, posing significant threats to users and organizations. Cybercriminals exploit malicious uniform resource locators (URLs) to disseminate harmful content, execute phishing schemes, and orchestrate various cyber attacks. As these threats evolve, detecting malicious URLs (MURLs) has become crucial for safeguarding internet users and ensuring a secure online environment. In response to this urgent need, we propose a novel machine learning-driven framework designed to identify known and unknown MURLs effectively. Our approach leverages a comprehensive dataset encompassing various labels—including benign, phishing, defacement, and malware—to engineer a robust set of features validated through extensive statistical analyses. The resulting malicious URL detection system (MUDS) combines supervised machine learning techniques, tree-based algorithms, and advanced data preprocessing, achieving a high detection accuracy of 96.83% for known MURLs. For unknown MURLs, the proposed framework utilizes CL_K-means, a modified k-means clustering algorithm, alongside two additional biased classifiers, achieving 92.54% accuracy on simulated zero-day datasets. With an average processing time of under 14 milliseconds per instance, MUDS is optimized for real-time integration into network endpoint systems. These outcomes highlight the efficacy and efficiency of the proposed MUDS in fortifying online security by identifying and mitigating MURLs, thereby reinforcing the digital landscape against cyber threats.

Cascaded capsule twin attentional dilated convolutional network for malicious URL detection

Malware is one of the most popular cyber-attacks, and it is becoming more common on the network every day. In contrast to benign transmission, which typically exhibits symmetrical patterns, malware communication often shows asymmetrical behaviours, making detection a complex challenge. Fortunately, malware can be distinguished and identified for actual activities utilizing a variety of artificial intelligence methods. However, insufficient work has been allocated to the problem of handling high-dimensional and huge data. This paper proposes a novel deep learning-based approach to identify malicious Uniform Resource Locators (URLs) specifically designed to handle the challenges posed by large-scale and complex data. Initially, input data is sourced from a comprehensive Kaggle dataset, which includes diverse and large-scale URL samples. The URLs are then transformed into vector representations using a Vector Embedding Module, which employs a character-level word embedding technique to capture intricate patterns within the URLs. To further refine the data, the Chaotic Kookaburra Efficient-Bo Network (CKEBO-Net) is applied to extract the most significant features from these vectors, effectively reducing the dimensionality and computational burden. Subsequently, the Cascaded Capsule Twin Attentional Dilated Convolutional Network (C2TA_DiCN) model is introduced to classify and identify malicious URLs with high precision. This model leverages the unique strengths of capsule networks and attentional mechanisms, enhancing its capability to capture subtle dependencies within the data. Furthermore, the Lyrebird Meta-heuristic Optimization (LMO) algorithm is used to fine-tune the model parameters appropriately, ensuring that the training process is efficient and robust. The proposed approach is implemented using Python and rigorously evaluated on the Kaggle dataset. Simulation results demonstrate that the proposed method significantly outperforms existing models, achieving a malicious URL detection accuracy of 99.7%.

Quality and readability of online information about piriformis syndrome

AimIn this study, we aimed to investigate the quality and readability of websites related to Piriformis Syndrome(PS). MethodOn November 14, 2023, we searched the term "Piriformis Syndrome" in Google (https://www.google.com). We evaluated the top 200 uniform resource locators(URLs) in query results for typology, Journal of the American Medical Association(JAMA) scores, the Flesch-Kincaid reading ease(FRE) score, Flesch-Kincaid grade(FKG) level, Gunning-Fog index, and Simple Measure of Gobbledygook(SMOG) grade level. The JAMA scoring system was used to assess quality, and FRE, FKG, Gunning-Fog index and SMOG scores were used to assess the readability of online information. Content analysis was performed for etiology, symptoms, diagnosis, and treatment. Treatment options were evaluated as exercise, physical therapy agents, drug therapy, invasive treatments, and surgery. ResultsOf the 138 websites evaluated, 39 were classified as high-quality websites. Scientific publications and health portal websites were found to have high-quality scores. When the readability parameters of the websites were compared in terms of quality, there was no significant difference between high-quality websites and low-quality websites in terms of FRE and FKG scores(p > 0.05), but there was a significant difference in Gunning-Fog index and SMOG scores(p = 0.030, p = 0.010 respectively). The quality score of the top 10 websites was higher than the rest(p = 0.002). When the contents of the websites were analyzed, there was a significant difference only in terms of diagnosis. Scientific websites mentioned diagnosis the most, while commercial websites mentioned it the least. When the treatment content was analyzed, there was a significant difference in terms of surgery. Non-profit websites mentioned it the most, while commercial websites mentioned it the least. ConclusionHigh-quality websites were slightly more difficult in the readability parameters. However, the proportion of high-quality websites was quite low. These results may encourage those producing PS-related online information to improve the quality and readability of content and content coverage.

Digital Fortress - Web Application Malware Detection

Currently, the risk of network information insecurity is increasing rapidly in number and level of danger. The method mostly used by hackers today is to attack end to end technology and exploit human vulnerabilities. These techniques include social engineering, phishing, pharming, etc. one of the steps in conducting these attacks is to deceive users with malicious Uniform Resource Locators (URLs). As results, malicious URL detection is of great interest nowadays. there have been several scientific studies showing a number of methods to detect malicious URLs based on machine learning and deep learning techniques. In this paper, we propose a malicious URL detection method using machine learning techniques based on our proposed URL behavior and attributes. moreover, bigdata technology is also exploited to improve the capability of detection malicious URLs based on abnormal behaviour. In short, the proposed detection system consists of a new set of URLs features and behavior, a machine learning algorithm, and a bigdata technology. the experimental results show that the proposed URL attributes and behaviour can help improve the ability to detect malicious URL significantly. This is suggested that the proposed system may be considered as an optimized and friendly used solution for malicious URL detection.

Encryption of URL Address (Uniform Resource Locator) on PHP Web Base with MD5 Algorithm

The problem of an information (web base) is the security of the database displayed by the website manager. A common problem that is often encountered in information systems is how to make data displayed publicly have secure access without having to destroy the data in the web base system itself. A cryptographic system or often called a cipher is a system or collection of rules used to perform encryption and decryption. There are two kinds of cryptographic systems, namely secret key cryptographic systems or often referred to as symmetric cryptographic systems and public key cryptographic systems or often referred to as asymmetric cryptographic systems. MD5 is a one-way hash function created by Ron Rivest. MD5 is an improvement over MD4 after MD4 was successfully attacked by cryptanalysts. The MD5 algorithm takes as input a message of arbitrary size and produces a message digest that is 128 bits long.

Illegal Online Gambling Site Detection using Multiple Resource-Oriented Machine Learning.

The COVID-19 pandemic has led to faster digitalization and illegal online gambling has become popular. As illegal online gambling brings not only financial threats but also breaches in overall cyber security, this study defines the concept of absolute illegal online gambling (AIOG) using a machine-learning-driven approach with information gathered from public webpages. By analysing 11,172 sites to detect illegal online gambling, the proposed model classifies key features such as URLs (Uniform Resource Locator), WHOIS, INDEX, and landing page information. With a combination of text and image analyses with machine learning-driven approach, the proposed model offers the ensemble combination of attributes for high detection performance with the verification of common attributes from metadata in online gambling. This study suggests a strategy for dynamic resource utilization to increase the classification accuracy of the current environment. As a result, this research expands the scope of hybrid web mining through constant updating of data to achieve content-based filtering.

URL shortener for web consumption: an extensive and impressive security algorithm

<p>URL stands for uniform resource locator are the addresses of the unique resources on the internet. We all need URLs to access any type of resource on the internet, such as any web page, and document. Sometimes URLs can be long, irrelative and unattractive and unable to send sometimes via email. So, for this, we proposed a URL shortener web application based on the Python-Django platform which is fast and makes your long URLs in the shortest form which you can share on social media platforms. It makes all the messy, unattractive URLs short and shareable. Writing paper proposed a premium section in our application that gives access to the customizable URLs and analytics of your shorten URLs. Customizable URLs are the URLs you create by your own keywords. By creating a premium profile with the application, you can create your own URLs by using your own keywords. We have considered security a major part of the application that prevents the short URLs from being hacked or redirected to any advertising website or content. We store all the data related to the URL to show you the best view of your analytics and update it regularly. Main contribution in this field that for web application that provides users with a fast, secure and shortest URL for their using long URLs. Comparatively to other services that are currently available, the application provides superior security, availability, and confidentiality.</p>

Web Site Accessibility Among Doctor of Physical Therapy Programs in the United States.

Given the impetus to improve accessibility for diverse learners seeking physical therapist education, it is critical that all entry points to access information have minimal barriers. This study identified Web site accessibility barriers among Doctor of Physical Therapy (DPT) programs in the United States. Web site accessibility has been evaluated among many institutions of higher education, but none focused on DPT education. Individuals with disabilities may be adversely affected by Web site accessibility barriers. This cross-sectional study included 262 DPT programs in the United States. Doctor of Physical Therapy program characteristics collected were geographic region, institutional control type (public/private), medical school affiliation, accreditation status, total institutional enrollment, and DPT class size. The Web Accessibility Evaluation (WAVE) Tool assessed data related to accessibility barriers among DPT program homepage Uniform Resource Locators. Three primary outcomes from the WAVE Tool included WAVE Total Errors, Error Density, and Total Alerts. Web site homepage accessibility barriers varied among programs for WAVE Total Errors (range 0-150), Error Density (range 0-14.6%), and Total Alerts (range 1-331). Median Total Errors were greater among private (9.0) versus public (5.0) institution Web sites (P < .001). Median Total Errors were greater among those institutions not affiliated with a medical school (9.0) compared with those that had an affiliated medical school (7.0) (P = .04). No differences in accessibility barriers were identified according to geographic region or accreditation status (P > .05). Median Total Errors were significantly different between institutional enrollment quartiles (H[3] = 17.9, P < .001), with no differences noted between DPT class size quartiles for any outcome (P > .05). Generally, weak-fair inverse correlations were observed between student enrollment for the institution and Web site accessibility barrier outcomes. Homepage accessibility barriers varied greatly among DPT programs in the United States. Factors, including being a private institution, no medical school affiliation, and lower institutional enrollment, were related to increased accessibility barriers.

OTHR-10. PILOT STUDY TO EVALUATE AUTOMATED LABORATORY DATA TRANSFER INTO MEDIDATA RAVE ACROSS THE NATIONAL CANCER INSTITUTE (NCI)-SUPPORTED PEDIATRIC BRAIN TUMOR CONSORTIUM (PBTC) AND PEDIATRIC EARLY PHASE CLINICAL TRIAL NETWORK (PEP-CTN) COOPERATIVE GROUPS

Abstract BACKGROUND Electronic data capture (EDC) has the potential to improve data accuracy and efficiency in oncology. The PBTC and PEP-CTN cooperative groups conducted a pilot project to 1) assess the feasibility of automated transfer of laboratory data from participating institutions’ electronic health records (EHR) to the Medidata Rave EDC housed within each consortium’s Uniform Resource Locator (URL); 2) evaluate discrepancies between retrospective automated laboratory data extraction from the EHR and manual data ascertainment during the original conduct of the study (MANUAL). METHODS Data from patients treated on 7 completed PBTC or PEP-CTN trials at 7 hospitals were included. Each consortium created a Rave instance to receive data. Sites elected to use local methods or the ExtractEHR R package to map and extract laboratory data for designated patients. Post-extraction data were uploaded to the Rave Batch Uploader (BU)-associated file transfer protocol (FTP) and BU uploaded data into Rave. After successful upload, discrepancies between EHR and MANUAL data were categorized. Two hospitals reviewed discrepancies at their sites to identify reasons for discrepancies. RESULTS Automated extraction and upload required significant PBTC/PEP-CTN operational effort and close work with hospital technical teams to map and extract data. All sites successfully extracted specified laboratory data and had ≥1 complete upload via BU to Rave; 2 sites had 2 uploads. Discrepancies were most commonly due to more data in EHR datasets (&amp;gt;1 result on a given day, data on additional study window days) or mismatched values or units. Differences in percentages of discrepant results between hospitals were likely due to mapping differences for EHR extraction. CONCLUSIONS This study demonstrated that in the pediatric cooperative oncology group setting automated laboratory data ascertainment can be performed. Accomplishing this required upfront central operational and hospital-based effort to prepare for data upload and transfer. This effort varied by site.

DETECTION OF UNHEALTHY WEBSITES USING MACHINE LEARNING

In recent years, advancements in Internet and cloud technologies have led to a signicant increase in electronic trading in which consumers make online purchases and transactions. Accompanying this achievement are vices like unauthorized access to users' sensitive information and damages to enterprise resources. Phishing is one of the familiar attacks that trick users to access malicious content and gain their information. This study aims to develop an efcient machine-learning program to detect phishing websites with high accuracy. Most phishing webpages look identical to the actual web pages and various strategies for detecting phishing websites, such as blacklisting, and heuristics, among others have been suggested. Existing research works showed that the performance of the phishing detection system is limited and there is a demand for intelligent techniques to protect users from cyber-attacks. A Uniform resource locator (URL) detection technique based on a supervised machine learning approach – Naïve Bayes is employed and implemented in Python programming language. The efcacy of this approach was determined on a phishing dataset made up of 7900 malicious and 5800 legitimate sites, respectively. The results show that using the proposed methodology an accuracy of 96% can be achieved by using stacking, ltering along the Naïve Bayes and logistic regression. This study thoroughly investigates the use of machine laearning with features extracted from the URLs and was able to showcase common words for the identication of either phishing (unhealthy) or good websites and proffered a guide to end users against the recent approaches in malicious URLs detection.

Pilot study to evaluate automated laboratory data transfer into Medidata Rave for NCI-supported pediatric cooperative group trials in the Pediatric Early Phase Clinical Trials Network (PEP-CTN) and Pediatric Brain Tumor Consortium (PBTC).

e23017 Background: Electronic data capture (EDC) has the potential to improve data accuracy and efficiency. As an initial step toward improvement in the accuracy and completeness of data acquisition for clinical trials, PEP-CTN and PBTC conducted a pilot project to assess the feasibility of automated transfer of laboratory data from participating institutions’ electronic health records to the NCI Medidata Rave EDC that is housed within each consortium’s Uniform Resource Locator (URL). The pilot aimed to assess feasibility of automated laboratory data extraction and transfer and to evaluate discrepancies between laboratory data obtained retrospectively through automated extraction and data manually ascertained during the original conduct of the study. Methods: Data from patients treated on 7 completed PEP-CTN or PBTC trials at 7 sites were included. Each consortium created a Rave EDC study to receive data. Sites used the ExtractEHR R package or local methods to map and extract laboratory data for designated patients. Post-extraction data were uploaded via a file transfer protocol (FTP) periodically polled by the Rave Batch Uploader (BU) that uploaded data into Rave EDC. After successful upload, extracted data (EHR) were compared to laboratory data that had been manually reported when patients were enrolled on the trials (MANUAL) and discrepancies were identified. Discrepancy types were categorized and 2 sites reviewed discrepancies at their sites to identify reasons. Results: Preparation and support for automated extraction and upload required significant central operations and local site effort. All sites successfully extracted specified laboratory data and had at least one complete upload via BU to Rave EDC. There were more laboratory data in the EHR datasets than MANUAL datasets across most sites. These results were due to multiple EHR results on a given day or from additional days in the study window than specified in MANUAL. Sites verified that laboratory data in MANUAL and not EHR were stored in un-extractable formats (e.g. PDFs) or outside laboratories. Most discrepant results in both EHR and MANUAL were mismatched values or units. Conclusions: This pilot demonstrated the feasibility of implementing automated laboratory data extraction and transfer in the cooperative group setting. Automated data transfer requires significant central operational support as well as detailed protocols, data specifications, and case report forms and engagement of site technical teams to be successful, but also permits collection of more comprehensive data with reduced manual data entry. Clinical trial information: NCT05020951 .

Real-time phishing detection using deep learning methods by extensions

Phishing is an attack method that relies on a user’s insufficient vigilance and understanding of the internet. For example, an attacker creates an online transaction website and tricks users into logging into the fake website to steal their personal information, such as credit card numbers, email addresses, phone numbers, and physical addresses. This paper proposes implementing an extension to prevent phishing for internet users. In particular, this study develops a smart warning feature for the proposed extension using deep learning models. The proposed extension installed in the web browser protects users by checking for, warning about, and preventing untrusted connections. This study evaluated and compared the performance of machine learning models using a malicious uniform resource locator (URL) dataset containing 651,191 data samples. The results of the investigation confirm that the proposed extension using a convolutional neural network (CNN) achieved a high accuracy of 98.4%.

CREATION OF WEB BROWSER USING PyQt5

A web browser is a software application for retrieving, presenting, and traversing information resources on the World Wide Web. The information resource is identified by a Uniform Resource Identifier (URI) and may be a web page, image, video, or other piece of content. In these project our major goal is to create a web browser using only PYQt5. The PyQt5 is one of the GUI Toolkit of applications of python programming language. PyQt5 is cross-platform GUI toolkit. One can develop an interactive desktop application with so much ease because of the tools and simplicity provided by this library. A GUI application consists of Front-end and Back-end. PyQt5 has provided a tool called ‘Qt Designer’ to design the front-end by drag and drop method so that development can become faster and one can give more time on back-end stuff. It shows an sample browser like Google with some extra features like home , reload, back ,forward and moreover it is easy for creating a browser with PyQt5,by using this library the most useful thing for engineers to code reducibility..

"VNeSafe: Machine Learning-Assisted System for Detecting Malicious URLs and Spam Calls"

Spam calls and malicious Uniform Resource Locators (URLs) have become major concerns for Internet users. Phishing, spam, and drive-by-download attacks can be initiated by malicious URLs, while normal users may experience irritation from spam calls. To tackle the aforementioned issues, we provide VNeSafe, a machine learning-assisted system, in this paper. By leveraging user feedback, VNeSafe may identify a phone number that is spam. Particularly, it keeps track of how many times a phone subscriber has been reported as spam. When such a number is over a predetermined threshold, VNeSafe automatically adds the phone number to a blacklist and blocks it. Furthermore, VNeSafe uses a natural language processing technique named TF-IDF in order to extract good features from a URL. The Random Forest algorithm then makes use of these features to determine whether the URL is malicious or not. Our empirical research has demonstrated that Random Forest can offer a real-time detection with an F1-score of 0.9298. This algorithm is ready to be deployed in VNeSafe and used on a general mobile device.

Hazard Identification and Detection Using Machine Learning Approach

Internet surfing has become a vital part of our daily life. So to catch the attention of the users' different browser vendors compete to set up the new functionality and advanced features that become the source of attacks for the intruder and the websites are put at hazard. However, the existing approaches are not adequate to protect the surfers which require an expeditious and precise model that can be able to distinguish between the begnign or malicious webpages. In this research article, we design a new classification system to analyze and detect the malicious web pages using machine learning classifiers such as, random forest, support vector machine. naïve Bayes, logistic regression and Some special URL (Uniform Resource Locator) based on extricated features the classifiers are trained to predict the malicious web pages. The experimental results have shown that the performance of the random forest classifier achieves better accuracy of 95% in comparison to other machine learning classifiers