A complete list of live websites using the Data-Driven Documents technology, compiled through global website indexing conducted by WebTechSurvey.

Results from Checkbot API to measure and collect 341 websites compatibility on multiple SEO variables (34 variables). Checkbot API indexes the website's code to find features capable of impacting SEO performance. Each website has been tested with the maximum number of links allowed to be crawled equally to 10.000 per test. In this way, we retrieved data about the overall websites performance including their sub-pages, and not only the main domain names. A scale from 0 (lowest rate) to 100 (highest rate) was adopted for each examined variable. This constitutes a useful managerial indicator of dealing with the quantification of websites performance while avoiding complex measurement systems that are difficult to be adopted by administrators. Websites tested were also categorized by the CMS type used. More information about the variables and the meaning of the results can be found at https://www.checkbot.io/

A database-driven web site on all living cephalopods (octopus, squid, cuttlefish and nautilus). Contact at cephbase@hotmail.com AccConID=21 AccConstrDescription=This license lets others distribute, remix, tweak, and build upon your work, even commercially, as long as they credit you for the original creation. This is the most accommodating of licenses offered. Recommended for maximum dissemination and use of licensed materials. AccConstrDisplay=This dataset is licensed under a Creative Commons Attribution 4.0 International License. AccConstrEN=Attribution (CC BY) AccessConstraint=Attribution (CC BY) Acronym=None added_date=2004-08-11 10:24:36 BrackishFlag=None CDate=2004-05-10 cdm_data_type=Other CheckedFlag=0 Citation=NRCC, University of Texas Medical Branch, Phillip Lee, and James B. Wood; Biology Department, Dalhousie University, Cartiona L. Day and Ronald K. O'Dor., CephBase (European data). National Resource Center for Cephalopods (NRCC), 11 Aug 2004, Galveston, Texas. Comments=None ContactEmail=None Conventions=COARDS, CF-1.6, ACDD-1.3 CurrencyDate=None DasID=18 DasOrigin=Literature research DasType=Data DasTypeID=1 DateLastModified={'date': '2025-08-12 01:34:46.196267', 'timezone_type': 1, 'timezone': '+02:00'} DescrCompFlag=0 DescrTransFlag=0 Easternmost_Easting=32.34 EmbargoDate=None EngAbstract=A database-driven web site on all living cephalopods (octopus, squid, cuttlefish and nautilus). Contact at cephbase@hotmail.com EngDescr=CephBase is a dynamic relational database-driven web site that has been online since 1998. CephBase provides taxonomic data, distribution, images, videos, predator and prey data, size, references and scientific contact information for all living species of cephalopods (octopus, squid, cuttlefish and nautilus) in an easy to access, user-friendly manner.

Species Database: Search by scientific, common name or synonym to call up species-specific pages with information such as full taxonomy, type species, names, size, predators, prey, biogeography, distribution maps, country lists, life history, images, videos, references, genetic information links and other internet resources.

Image Database: Search our ~1650 cephalopod images which cover all life stages, behaviour, ecology, taxonomy as well as many other aspects of these amazing animals. Each image has a caption, key words, location, photographer and other data.

Video Database: There are ~150 video clips in the video database.

Reference Database: There are now over 6000 ceph papers in our reference database.

Researcher Directory: Looking for a grad school supervisor or cephalopod expert? There are over 400 names in the International Directory of Cephalopod Workers.

Predators and Prey: Search by predator, prey or cephalopod species in our predators and prey databases.

Biogeography: In collaboration with The Sea Around Us Project (Daniel Pauly, Principal Investigator), the species-specific occurrence records already in CephBase and geographical distributions of commercial species in the 1984 FAO Species Catalogue, have been allocated to 18 FAO Statistical Areas, 64 Large Marine Ecosystems and the Exclusive Economic Zones of about 200 maritime countries and territories. See the Biogeography page. Links to country lists are available on each species page, where applicable. Plots of the occurrence records can now be plotted with either the C-Squares Mapper (courtesy of Tony Rees, CSIRO) or the OBIS Specimen Mapper (courtesy of the Kansas Geological Survey and the Hexacorallia Project) and the distribution range maps can be viewed.

You can also view feedback we have received, FAQ's, links, our collaborators and the CIAC beak database in CephBase.

The CephBase project was created in 1998 by Dr. James Wood and Catriona Day, at Dalhousie University (Dr. Ron O'Dor, Principal Investigator), supported by the National Oceanographic Partnership Program. Since 2000, the database has been housed at the National Resource Center for Cephalopods at the University of Texas Medical Branch (Dr. Phil Lee, Principal Investigator) and continues to be maintained by Catriona Day at the UBC Fisheries Centre, Vancouver, BC. CephBase is part of the Census of Marine Life, an international program to explain the diversity, distribution and abundance of marine life. FreshFlag=None geospatial_lat_max=81.25 geospatial_lat_min=27.45 geospatial_lat_units=degrees_north geospatial_lon_max=32.34 geospatial_lon_min=-45.0 geospatial_lon_units=degrees_east infoUrl=None InputNotes=None institution=BBSR License=https://creativecommons.org/licenses/by/4.0/ Lineage=Prior to publication data undergo quality control checked which are described in https://github.com/EMODnet/EMODnetBiocheck?tab=readme-ov-file#understanding-the-output MarineFlag=0 modified_sync=2021-02-04 00:00:00 Northernmost_Northing=81.25 OrigAbstract=None OrigDescr=None OrigDescrLang=None OrigDescrLangNL=None OrigLangCode=None OrigLangCodeExtended=None OrigLangID=None OrigTitle=None OrigTitleLang=English OrigTitleLangCode=en OrigTitleLangID=15 OrigTitleLangNL=Engels Progress=In Progress PublicFlag=1 ReleaseDate=Aug 11 2004 12:00AM ReleaseDate0=2004-08-11 RevisionDate=None SizeReference=786 species sourceUrl=(local files) Southernmost_Northing=27.45 standard_name_vocabulary=CF Standard Name Table v70 StandardTitle=CephBase StatusID=1 subsetVariables=ScientificName,aphia_id TerrestrialFlag=0 UDate=2025-03-26 VersionDate=Feb 25 2004 12:00AM VersionDay=24 VersionMonth=5 VersionName=1 VersionYear=2006 VlizCoreFlag=1 Westernmost_Easting=-45.0

Description The datasets demonstrate the malware economy and the value chain published in our paper, Malware Finances and Operations: a Data-Driven Study of the Value Chain for Infections and Compromised Access, at the 12th International Workshop on Cyber Crime (IWCC 2023), part of the ARES Conference, published by the International Conference Proceedings Series of the ACM ICPS. Using the well-documented scripts, it is straightforward to reproduce our findings. It takes an estimated 1 hour of human time and 3 hours of computing time to duplicate our key findings from MalwareInfectionSet; around one hour with VictimAccessSet; and minutes to replicate the price calculations using AccountAccessSet. See the included README.md files and Python scripts. We choose to represent each victim by a single JavaScript Object Notation (JSON) data file. Data sources provide sets of victim JSON data files from which we've extracted the essential information and omitted Personally Identifiable Information (PII). We collected, curated, and modelled three datasets, which we publish under the Creative Commons Attribution 4.0 International License. 1. MalwareInfectionSet We discover (and, to the best of our knowledge, document scientifically for the first time) that malware networks appear to dump their data collections online. We collected these infostealer malware logs available for free. We utilise 245 malware log dumps from 2019 and 2020 originating from 14 malware networks. The dataset contains 1.8 million victim files, with a dataset size of 15 GB. 2. VictimAccessSet We demonstrate how Infostealer malware networks sell access to infected victims. Genesis Market focuses on user-friendliness and continuous supply of compromised data. Marketplace listings include everything necessary to gain access to the victim's online accounts, including passwords and usernames, but also detailed collection of information which provides a clone of the victim's browser session. Indeed, Genesis Market simplifies the import of compromised victim authentication data into a web browser session. We measure the prices on Genesis Market and how compromised device prices are determined. We crawled the website between April 2019 and May 2022, collecting the web pages offering the resources for sale. The dataset contains 0.5 million victim files, with a dataset size of 3.5 GB. 3. AccountAccessSet The Database marketplace operates inside the anonymous Tor network. Vendors offer their goods for sale, and customers can purchase them with Bitcoins. The marketplace sells online accounts, such as PayPal and Spotify, as well as private datasets, such as driver's licence photographs and tax forms. We then collect data from Database Market, where vendors sell online credentials, and investigate similarly. To build our dataset, we crawled the website between November 2021 and June 2022, collecting the web pages offering the credentials for sale. The dataset contains 33,896 victim files, with a dataset size of 400 MB. Credits Authors Billy Bob Brumley (Tampere University, Tampere, Finland) Juha Nurmi (Tampere University, Tampere, Finland) Mikko Niemelä (Cyber Intelligence House, Singapore) Funding This project has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme under project numbers 804476 (SCARE) and 952622 (SPIRS). Alternative links to download: AccountAccessSet, MalwareInfectionSet, and VictimAccessSet.

Unlock the power of data-driven marketing! Explore the booming Marketing Data Analysis Software market, projected to reach [estimated 2033 value] by 2033. Discover key trends, leading companies, and regional insights to optimize your marketing strategies and achieve higher ROI. Learn more about software types, applications, and future growth potential.

The statistic represents to which extent French companies store and use their client data in 2019. The study compared data driven companies who already store their client information and use their data as a mean of transaction growth and non-data driven companies who do not yet orient themselves around client data. From the non-data driven companies, none of them tracked their users responsiveness to e-mail campaigns or other forms of advertisements and webpage visits. Of the data driven companies, 100 percent tracked their client contact information as opposed to ** percent from the non-data driven companies. Client orders were tracked by ** percent of the data driven companies compared to ** percent of the non-data driven ones. The details of the purchased products played to ** percent an important role for data driven companies who also fully tracked their website visits.

This document provides an overview of the accompanying data files used in the manuscript entitled "Practical data-driven flood forecasting based on dynamical systems theory: Case studies from Japan."

db_kagetsu.csv: Past hourly data on Kagetsu gauging station and 4 precipitation stations (Tsurukochi, Kagetsu, Yokohata, and Mikuma) downloaded from the website of the Water Information System (http://www1.river.go.jp/).

db_hiwatashi.csv Past hourly data on 5 gauging stations (Takeshita, Otobou, Hirose, Ohjibashi, and Hiwatashi) and 14 precipitation stations (Sunoura, Nojiri, Kensetsutakaharu, Shika, Sano, Kirishima, Miike, Hiwatashi, Aoidake, Mimata, Kabayama, Takeshita, Hisokino, and Sueyoshi) downloaded from the website of the Water Information System (http://www1.river.go.jp/).

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Context

For some, statistical analysis increases the enjoyment of sport. My long-term aim is to build an automated database driven advanced stats website like many that have come and gone before however that first starts with statistical analysis of the game to assess what and how much individual actions contribute to the outcome of the game.

The data represents all the official metrics measured for each game in the NHL in the past 6 years. I intend to update it semi-regularly depending on development progress of my database server.

Content

This is a mostly rational database, please refer to the "table_realtionships.jpg" for details on how the tables can be joined. This is not just the results and player stats of NHL games but also details on individual plays such as shots, goals and stoppages including date & time and x,y coordinates.

The dataset is incomplete, there are some games where no plays information is available on NHL.com. It is rare and I do not know the reasons.

Acknowledgements

Thanks to Kevin Sidwar who began documenting the still un-documented NHL stats API which was used to gather this data.

Inspiration

Compared to other sports, advanced statistics in Hockey are still in infancy. It has been suggested that the best models can only predict the winner 62% of the time due to variances in talent and "puck luck".

I would like to believe feature engineering and a suitably trained model can account for some of this variance and beat this seemingly low target.

Otherwise, what metrics can be developed to provide better indications than Corsi & Fenwick?

CephBase is a dynamic html (dhtml) relational database-driven interactive web site. The prototype version of CephBase was developed at Dalhousie Univeristy in Halifax, Canada and was sponsored by the Sloan Foundation following the Workshop on Non-Fish Nekton in Boston, December, 1997. As of 12/2000, CephBase has all the taxa, authorities, and the year the taxa was described online for all of the 703 known living species of cephalopods listed by Sweeney and Roper (1998). Information on a particular species can be quickly located by using the search engine; results are listed in table format. Users simply click on a species in the table and all the taxonomy, from class to subspecies, for that cephalopod is displayed. Users can also display an alphabetized list of all cephalopod genera. Clicking on a genus leads to a list of all species it contains. For each species, synonymies, type repositories, type localities, references and common names are listed. References are listed in abbreviated form with access to full references.

The Top Websites in the World

How They Change Over Time

About this dataset

This dataset consists of the top 50 most visited websites in the world, as well as the category and principal country/territory for each site. The data provides insights into which sites are most popular globally, and what type of content is most popular in different parts of the world

How to use the dataset

This dataset can be used to track the most popular websites in the world over time. It can also be used to compare website popularity between different countries and categories

Research Ideas

• To track the most popular websites in the world over time
• To see how website popularity changes by region
• To find out which website categories are most popular

Acknowledgements

Dataset by Alexa Internet, Inc. (2019), released on Kaggle under the Open Data Commons Public Domain Dedication and License (ODC-PDDL)

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: df_1.csv | Column name | Description | |:--------------------------------|:---------------------------------------------------------------------| | Site | The name of the website. (String) | | Domain Name | The domain name of the website. (String) | | Category | The category of the website. (String) | | Principal country/territory | The principal country/territory where the website is based. (String) |

A. Market Research and Analysis: Utilize the Tripadvisor dataset to conduct in-depth market research and analysis in the travel and hospitality industry. Identify emerging trends, popular destinations, and customer preferences. Gain a competitive edge by understanding your target audience's needs and expectations.

B. Competitor Analysis: Compare and contrast your hotel or travel services with competitors on Tripadvisor. Analyze their ratings, customer reviews, and performance metrics to identify strengths and weaknesses. Use these insights to enhance your offerings and stand out in the market.

C. Reputation Management: Monitor and manage your hotel's online reputation effectively. Track and analyze customer reviews and ratings on Tripadvisor to identify improvement areas and promptly address negative feedback. Positive reviews can be leveraged for marketing and branding purposes.

D. Pricing and Revenue Optimization: Leverage the Tripadvisor dataset to analyze pricing strategies and revenue trends in the hospitality sector. Understand seasonal demand fluctuations, pricing patterns, and revenue optimization opportunities to maximize your hotel's profitability.

E. Customer Sentiment Analysis: Conduct sentiment analysis on Tripadvisor reviews to gauge customer satisfaction and sentiment towards your hotel or travel service. Use this information to improve guest experiences, address pain points, and enhance overall customer satisfaction.

F. Content Marketing and SEO: Create compelling content for your hotel or travel website based on the popular keywords, topics, and interests identified in the Tripadvisor dataset. Optimize your content to improve search engine rankings and attract more potential guests.

G. Personalized Marketing Campaigns: Use the data to segment your target audience based on preferences, travel habits, and demographics. Develop personalized marketing campaigns that resonate with different customer segments, resulting in higher engagement and conversions.

H. Investment and Expansion Decisions: Access historical and real-time data on hotel performance and market dynamics from Tripadvisor. Utilize this information to make data-driven investment decisions, identify potential areas for expansion, and assess the feasibility of new ventures.

I. Predictive Analytics: Utilize the dataset to build predictive models that forecast future trends in the travel industry. Anticipate demand fluctuations, understand customer behavior, and make proactive decisions to stay ahead of the competition.

J. Business Intelligence Dashboards: Create interactive and insightful dashboards that visualize key performance metrics from the Tripadvisor dataset. These dashboards can help executives and stakeholders get a quick overview of the hotel's performance and make data-driven decisions.

Incorporating the Tripadvisor dataset into your business processes will enhance your understanding of the travel market, facilitate data-driven decision-making, and provide valuable insights to drive success in the competitive hospitality industry

Background For a long time one could not imagine being able to identify species on the basis of genotype only as there were no technological means to do so. But conventional phenotype-based identification requires much effort and a high level of skill, making it almost impossible to analyze a huge number of organisms, as, for example, in microbe-related biological disciplines. Comparative analysis of 16S rRNA has been changing the situation, however. We report here an approach that will allow rapid and accurate phylogenetic comparison of any unknown strain to all known type strains, enabling tentative assignments of strains to species. The approach is based on two main technologies: genome profiling and Internet-based databases. Results A complete procedure for provisional identification of species using only their genomes is presented, using random polymerase chain reaction, temperature-gradient gel electrophoresis, image processing to generate 'species-identification dots' (spiddos) and data processing. A database website for this purpose was also constructed and operated successfully. The protocol was standardized to make the system reproducible and reliable. The overall methodology thus established has remarkable aspects in that it enables non-experts to obtain an initial species identification without a lot of effort and is self-developing; that is, species can be determined more definitively as the database is used more and accumulates more genome profiles. Conclusions We have devised a methodology that enables provisional identification of species on the basis of their genotypes only. It is most useful for microbe-related disciplines as they face the most serious difficulties in species identification.

Snapshot img

Web Development Market Size 2025-2029

The web development market size is forecast to increase by USD 40.98 billion at a CAGR of 10.4% between 2024 and 2029.

The market is experiencing significant growth, driven by the increasing digital transformation across industries and the integration of artificial intelligence (AI) into web applications. This trend is fueled by the need for businesses to enhance user experience, streamline operations, and gain a competitive edge in the market. Furthermore, the rapid evolution of technologies such as Progressive Web Apps (PWAs), serverless architecture, and the Internet of Things (IoT) is creating new opportunities for innovation and expansion. However, this market is not without challenges. The ever-changing technological landscape requires web developers to continuously update their skills and knowledge. Additionally, ensuring web applications are secure and compliant with data protection regulations is becoming increasingly complex.
Companies seeking to capitalize on market opportunities and navigate challenges effectively should focus on building a team of skilled developers, investing in continuous learning and development, and prioritizing security and compliance in their web development projects. By staying abreast of the latest trends and technologies, and adapting quickly to market shifts, organizations can successfully navigate the dynamic the market and drive business growth.

What will be the Size of the Web Development Market during the forecast period?

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The market continues to evolve at an unprecedented pace, driven by advancements in technology and shifting consumer preferences. Key trends include the adoption of Agile methodologies, DevOps tools, and version control systems for streamlined project management. JavaScript frameworks, such as React and Angular, dominate front-end development, while Magento, Shopify, and WordPress lead in content management and e-commerce. Back-end development sees a rise in Python, PHP, and Ruby on Rails frameworks, enabling faster development and more efficient scalability. Interaction design, user-centered design, and mobile-first design prioritize user experience, while security audits, penetration testing, and disaster recovery solutions ensure website safety.
Marketing automation, email marketing platforms, and CRM systems enhance digital marketing efforts, while social media analytics and Google Analytics provide valuable insights for data-driven decision-making. Progressive enhancement, headless CMS, and cloud migration further expand the market's potential. Overall, the market remains a dynamic, innovative space, with continuous growth fueled by evolving business needs and technological advancements.

How is this Web Development Industry segmented?

The web development industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  Retail and e-commerce
  BFSI
  IT and telecom
  Healthcare
  Others


Business Segment

  SMEs
  Large enterprise


Service Type

  Front-End Development
  Back-End Development
  Full-Stack Development
  E-Commerce Development


Deployment Type

  Cloud-Based
  On-Premises


Technology Specificity

  JavaScript
  Python
  PHP
  Ruby


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Spain
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By End-user Insights

The retail and e-commerce segment is estimated to witness significant growth during the forecast period. The market is experiencing significant growth due to the digital transformation sweeping various industries. E-commerce and retail sectors lead the market, driven by the increasing preference for online shopping and improved Internet penetration. To cater to this trend, businesses demand user-engaging web applications with smooth navigation, secure payment gateways, and seamless product search and purchase features. Mobile shopping's rise necessitates mobile app development and mobile-optimized websites. Agile development, microservices architecture, and UI/UX design are essential elements in creating engaging and efficient web solutions. Furthermore, AI, machine learning, and data analytics enable data-driven decision making, customer loyalty, and business intelligence.

Web hosting, cloud computing, API integration, and growth hacking are other critical components. Ensuring web accessibility, data security, and e-commerce development is also crucial for businesses in the digital age. Online advertising, email marketing, content strategy, brand building, and data visualization are essential aspects of digital marketing. Serverless computing, u

Discover the booming website visitor tracking software market! Our analysis reveals a $5 billion market in 2025, projected to reach $15 billion by 2033, driven by digital marketing, data-driven decisions, and AI-powered analytics. Learn about key players, market trends, and regional insights.

Discover the booming Website Personalization Tool market! Learn about its $5 billion valuation, 15% CAGR, key drivers, leading companies (like Optimizely, VWO, and Personalize), and future trends shaping this dynamic sector. Boost your ROI with personalized website experiences.

Florabank1 is a database that contains distributional data on the wild flora (indigenous species, archeophytes and naturalised aliens) of Flanders and the Brussels Capital Region. It holds about 3 million records of vascular plants, dating from 1800 till present. Furthermore, it includes ecological data on vascular plant species, redlist category information, Ellenberg values, legal status, global distribution, seed bank etc. The database is an initiative of "Flo.Wer" (http://www.plantenwerkgroep.be), the Research Institute for Nature and Forest (INBO) (http://www.inbo.be) and the National Botanic Garden of Belgium (http://www.br.fgov.be). Florabank aims at centralizing botanical distribution data gathered by both professional and amateur botanists and to make these data available to the benefit of nature conservation, policy and scientific research. The occurrence data contained in Florabank1 are extracted from checklists, literature and herbarium specimen information. Of survey lists, the locality name (verbatimLocality), species name, observation date and IFBL square code - the grid system used for plant mapping in Belgium (Van Rompaey 1943) - are recorded. For records dating from the period 1972–2004 all pertinent botanical journals dealing with Belgian flora were systematically screened. Analysis of herbarium specimens in the collection of the National Botanic Garden of Belgium, the University of Ghent and the University of Liège provided interesting distribution knowledge concerning rare species, this information is also included in Florabank1.

The IFBL data recorded before 1972 is available through the Belgian GBIF node (http://www.gbif.org/dataset/940821c0-3269-11df-855a-b8a03c50a862), not through Florabank1, to avoid duplication of information. A dedicated portal providing access to all currently published Belgian IFBL records is available at: http://projects.biodiversity.be/ifbl.

All data in Florabank1 is georeferenced. Every record holds the decimal centroid coordinates of the > IFBL square containing the observation. The uncertainty radius is the smallest circle possible covering the whole IFBL square, which can measure 1 km² or 4 km². Florabank is a work in progress and new occurrences are added as they become available; the dataset will be updated through GBIF on a regularly base.

The global Enterprise Website Analytics Software market is experiencing robust growth, driven by the increasing need for businesses to understand their online presence and optimize their digital strategies. The market's expansion is fueled by several key factors, including the rising adoption of cloud-based solutions offering scalability and cost-effectiveness, the proliferation of mobile devices and diverse digital channels requiring sophisticated analytics, and a growing focus on data-driven decision-making across all departments. Large enterprises are leading the adoption, leveraging these tools for detailed customer journey mapping, performance optimization, and enhanced ROI on marketing investments. However, the market faces challenges such as the complexity of integrating various analytics platforms and the need for specialized expertise to effectively interpret and utilize the vast amounts of data generated. The segment showing the fastest growth is likely cloud-based solutions due to their flexibility and accessibility. We estimate the 2025 market size to be around $15 billion, based on observable growth trends in related software markets and considering the increasing adoption of analytics solutions across various industries. A Compound Annual Growth Rate (CAGR) of 12% is projected for the forecast period (2025-2033), indicating substantial market expansion over the coming years. The competitive landscape is highly dynamic, with both established tech giants (Google, IBM) and specialized analytics providers (Adobe, SEMrush, Mixpanel) vying for market share. The ongoing trend towards mergers and acquisitions further shapes the industry. Companies are continually innovating to offer more comprehensive solutions, incorporating features like artificial intelligence (AI) for predictive analytics, real-time data visualization, and seamless integration with CRM systems. Geographic growth will vary, with North America and Europe expected to maintain significant market share due to high technological adoption rates. However, Asia-Pacific is projected to witness substantial growth driven by increasing digitalization and economic expansion. The market's future trajectory hinges on continuous innovation within analytics capabilities, addressing the challenges of data privacy and security, and fostering greater user-friendliness within these sophisticated platforms.

Phishing Websites Dataset

Overview

This dataset is designed to aid in the analysis and detection of phishing websites. It contains various features that help distinguish between legitimate and phishing websites based on their structural, security, and behavioral attributes.

Dataset Information

• Total Columns: 31 (30 Features + 1 Target)
• Target Variable: Result (Indicates whether a website is phishing or legitimate)

Features Description

URL-Based Features

• Prefix_Suffix – Checks if the URL contains a hyphen (-), which is commonly used in phishing domains.
• double_slash_redirecting – Detects if the URL redirects using //, which may indicate a phishing attempt.
• having_At_Symbol – Identifies the presence of @ in the URL, which can be used to deceive users.
• Shortining_Service – Indicates whether the URL uses a shortening service (e.g., bit.ly, tinyurl).
• URL_Length – Measures the length of the URL; phishing URLs tend to be longer.
• having_IP_Address – Checks if an IP address is used in place of a domain name, which is suspicious.

Domain-Based Features

• having_Sub_Domain – Evaluates the number of subdomains; phishing sites often have excessive subdomains.
• SSLfinal_State – Indicates whether the website has a valid SSL certificate (secure connection).
• Domain_registeration_length – Measures the duration of domain registration; phishing sites often have short lifespans.
• age_of_domain – The age of the domain in days; older domains are usually more trustworthy.
• DNSRecord – Checks if the domain has valid DNS records; phishing domains may lack these.

Webpage-Based Features

• Favicon – Determines if the website uses an external favicon (which can be a sign of phishing).
• port – Identifies if the site is using suspicious or non-standard ports.
• HTTPS_token – Checks if "HTTPS" is included in the URL but is used deceptively.
• Request_URL – Measures the percentage of external resources loaded from different domains.
• URL_of_Anchor – Analyzes anchor tags (<a> links) and their trustworthiness.
• Links_in_tags – Examines <meta>, <script>, and <link> tags for external links.
• SFH (Server Form Handler) – Determines if form actions are handled suspiciously.
• Submitting_to_email – Checks if forms submit data directly to an email instead of a web server.
• Abnormal_URL – Identifies if the website’s URL structure is inconsistent with common patterns.
• Redirect – Counts the number of redirects; phishing websites may have excessive redirects.

Behavior-Based Features

• on_mouseover – Checks if the website changes content when hovered over (used in deceptive techniques).
• RightClick – Detects if right-click functionality is disabled (phishing sites may disable it).
• popUpWindow – Identifies the presence of pop-ups, which can be used to trick users.
• Iframe – Checks if the website uses <iframe> tags, often used in phishing attacks.

Traffic & Search Engine Features

• web_traffic – Measures the website’s Alexa ranking; phishing sites tend to have low traffic.
• Page_Rank – Google PageRank score; phishing sites usually have a low PageRank.
• Google_Index – Checks if the website is indexed by Google (phishing sites may not be indexed).
• Links_pointing_to_page – Counts the number of backlinks pointing to the website.
• Statistical_report – Uses external sources to verify if the website has been reported for phishing.

Target Variable

• Result – The classification label (1: Legitimate, -1: Phishing)

Usage

This dataset is valuable for:
✅ Machine Learning Models – Developing classifiers for phishing detection.
✅ Cybersecurity Research – Understanding patterns in phishing attacks.
✅ Browser Security Extensions – Enhancing anti-phishing tools.

This dataset represents a collection of records for users' visits to a website, where certain variables related to these users are studied to determine whether they clicked on a particular ad or not. Here’s a detailed description of the data:

Daily Time Spent on Site: The number of minutes the user spends on the website daily.

Age: The age of the user in years.

Area Income: The average annual income of the area where the user resides, measured in U.S. dollars.

Daily Internet Usage: The number of minutes the user spends on the internet daily.

Ad Topic Line: The headline or main topic of the ad that was shown to the user.

City: The city where the user resides.

Male: An indicator of the user's gender, where 1 represents male and 0 represents female.

Country: The country where the user resides.

Timestamp: The date and time when this record was logged.

Clicked on Ad: An indicator of whether the user clicked on the ad, where 1 means the user clicked on the ad, and 0 means they did not.

In summary, this data is used to analyze users' behavior on the website based on a set of demographic and usage factors, with a focus on whether they clicked on a particular ad or not.