phishing url dataset github

Legitimate Dataset : Legitimate URLs were prepared by the following steps: A balanced dataset with 10,000 legitimate and 10,000 phishing URLs and an imbalanced dataset with 50,000 legitimate and 5,000 phishing URLs were prepared. The present paper proposes a URL feature-based approach to get these websites detected and predicted as if they are phishing websites or non-phishing ones. 2). In this work, we constructed a dataset of about 1.5 million URLs with 51% of them as legitimate and 49% of them as phishing. Update from 2017: "Phishing via email was the most prevalent variety of social attacks" Social attacks were utilized in 43% of all breaches in the 2017 dataset. Each instance contains the URL and the relevant HTML page. rec_id - record number This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Check if oliv.github.io is legit website or scam website URL checker is a free tool to detect malicious URLs including malware, scam and phishing links. Even with adequate training and high situational awareness, it can still be hard for users to continually be aware of the URL of the website they are visiting. 2 files Description The dataset consists of a collection of legitimate as well as phishing website instances. TLDs can be categorized into gTLDs (generic TLDs) that are maintained by the Internet Assigned Numbers Authority (IANA) for use in the Domain Name Systems of the Internet, and ccTLDs (country code TLDs) that are usually reserved for specific geographic locations. 5). This is because most Phishing attacks have some common characteristics which can be identified by machine learning methods. Paper is available @.https://doi.org/10.1145/3486622.3493983. legitimate domains were chosen randomly from a set of domains included in the IP2Location dataset consistently from January 2021 to March 2021, Each chosen domain was accessed by Apache Nutch crawler to gather the web pages located in the same domain at most 100 pages, and. Clean data using customised Python code. - The URLs were collected from the above sources and fetched the relevant webpages separately. OpenPhish - From 29 September 2021 to 31 October 2021 Use Git or checkout with SVN using the web URL. adaptability to any other forms (for example, embedding URLs in spam messages or emails). URL - http://phishing-url-detector-api.herokuapp.com/. Table 2 provides the statistics of our dataset. The dataset consists of a collection of legitimate as well as phishing website instances. The most common TLDs (top-level domains) are .com and .net in our dataset. You signed in with another tab or window. If nothing happens, download Xcode and try again. Thus, recently, researchers tend to focus on information- Although many methods have been proposed to detect phishing websites, Phishers have evolved their methods to escape from these detection methods. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. In phishing URL detection, feature engineering is a crucial yet challenging way to improve performance. Do try it out. A tag already exists with the provided branch name. The index.sql file is the root file, and it can be used to map the URLs with the relevant HTML pages. They extracted 14 different features, which make phishing websites different from legitimate websites. Phishing is considered to be one of the most prevalent cyber-attacks because of its immense flexibility and alarmingly high success rate. One of the most successful methods for detecting these malicious activities is Machine Learning. Label 0 represents Legitimate URL Label 1 represents Phishing URL There is 702 phishing URLs, and 103 suspicious URLs. Phishing website dataset. Table 1 exemplifies five legitimate URLs and five phishing URLs in our dataset. Almost all phishing attacks that led to a breach were followed with some form of malware, and 28% of phishing breaches were targeted. Apply. In fact this challenge faces any researcher in the field. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Short description of the full variant dataset: Total number of instances: 88,647 Phishing attacks cause severe economic damage around the world. This section . Data. The phishing detection method focused on the learning process. We use the PyFunceble testing tool to validate the status of all known Phishing domains and provide stats to reveal how many unique domains used for Phishing are still active. ENVIRONMENTS: Microsoft Defender for O365. - Number of phishing website instances (labelled as 1 in the SQL file): 30,000 The URL dataset is taken from the UCI machine learning repository . Several organizations maintain and publish free blocklists of IP addresses and URLs of systems and networks suspected in malicious activities on-line. 2. The above mentioned datasets are uploaded to the ' DataFiles ' folder of this repository. - An automated script continuously monitored PhishTank and OpenPhish to collect the latest phishing URLs. 4). Highlights: 4. In phishing detection, an incoming URL is identified as phishing or not by analysing the different features of the URL and is classified accordingly. The attributes of the prepared dataset can be divided into six groups: Google search - Simple keyword search on the google search engine was used, and the top 5 URLs of each search were collected. Are you sure you want to create this branch? If nothing happens, download Xcode and try again. There was a problem preparing your codespace, please try again. - The URLs were collected from the above sources, and at the same time, the relevant web pages were fetched. And the second dataset has been taken from Kaggle Repository (Phishing website dataset | Kaggle 2020). Rami M. Mohammad, Fadi Thabtah, and Lee McCluskey have even used neural nets and various other models to create a really robust phishing detection system. search. The dataset in total features 111 attributes ex cluding the target phishing attribute, which de- notes whether the particular ins tance is legitimate (value 0) or phishing (value 1). Full variant - dataset_full.csv Short description of the full variant . Accessed 31 October 2021. [3]. The OpenPhish Database is provided as an SQLite database and can be easily integrated into existing systems using our free, open-source API module . Work fast with our official CLI. Creating this notebook helped me to learn a lot about the features affecting the models to detect whether URL is safe or not, also I came to know how to tuned model and how they affect the model performance. Updated 4 years ago. result - Indicates whether a given URL is phishing or not (0 for legitimate and 1 for phishing). Phishing Dataset : We collected phishing URLs from PhishTank , the most popular site distributing phishing websites, from May 2021 to June 2021. A URL based phishing attack is carried out by sending malicious links, that seems legitimate to the users, and tricking them into clicking on it. Some Phishing Webpages successfully detected by Malicious URL Detector, https://mudvfinalradar.eu-gb.cf.appdomain.cloud/, https://mudvfinalradar.eu-gb.cf.appdomain.cloud/fetchanalysis, https://github.com/abhisheksaxena1998/ChromeExtension-Malicious-URL-v5-IBM, https://github.com/Hritiksum/MUD_dataset/blob/master/Training%20and%20Testing%20Model/Training%20and%20Testing.ipynb, https://www.airtelxstream.in/livetv-channels/sony-sab/mwtv_livetvchannel_347, https://myjiocare.com/sony-liv-premium-account-free/, https://www.youtube.com/watch?v=dnbkysr3hoo, markmonitor.comwhoisrequest@markmonitor.com, https://www.youtube.com/watch?v=pyc61thl3o8, abuse-contact@publicdomainregistry.comnsk.rockstar97@. The objective of this notebook is to collect data & extract the. 3). Learn more. Ebbu2017 Phishing Dataset. Web application. You have built a machine learning model that predicts if a URL is a phishing one. Usability. Sources: Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. This dataset was donated by Rami Mustafa A Mohammad for further analysis. The dataset is designed to be used as benchmarks for machine learning-based phishing detection systems. If nothing happens, download GitHub Desktop and try again. In terms of website interface and uniform resource locator (URL), most phishing webpages look identical to the actual webpages. Life is dependent mainly on internet in todays life for moving business online, or making online transactions. Most Internet users refer to it as the "address for a website". There was a problem preparing your codespace, please try again. The dataset can serve as an input for the machine learning process. 1635698138155948.html) It consisted of five fields. Zipped Training Dataset of 1.2 million records. Result Dataset. 1). In this repository the two variants of the Phishing Dataset are presented. Gradient Boosting Classifier currectly classify URL upto 97.4% respective classes and hence reduces the chance of malicious attachments. Out of all these types, the benign url dataset is considered for this project. A fraudulent domain or phishing domain is an URL scheme that looks suspicious for a variety of reasons. This is because most Phishing attacks have some common characteristics which can be identified by machine learning methods. URLs are used as the main vehicle in this domain. Work fast with our official CLI. 1.5 million URLs with 51% of them as legitimate and 49% of them as phishing. While successful in protecting users from known malicious domains . Please send us an email from a domain owned by your organization for more information and pricing details. Unzip to 'csv' before use. To preview the dataset interactively and/or tailor it to your needs, please visit a dedicated web application. To counter this issues security community focused its efforts on developing techniques for mostly blacklisting of malicious URLs. The final take away form this project is to explore various machine learning models, perform Exploratory Data Analysis on phishing dataset and understanding their features. Created Jan 16, 2022 Extract URL, URL's length and HTTPS status using customised Python code. - Legitimate Data [50,000] - These data were collected from two sources. When clicked on, phishing URLs take you to fake websites, download malware or prompt for credentials. Thumbnail view List view File view. JPCERT/CC releases a URL dataset of phishing sites confirmed from January 2019 to June 2022, as we received many requests for more specific information after publishing a blog article on trends of phishing sites and compromised domains in 2021. Phishing URL Dataset collected from IP2Loaction and PhishTank. Attribute Information: URL Anchor Request URL close. According to APWG report [3], 165772 phishing sites have been detected in the rst quarter of 2020 and 162155 phishing sites have been identied in last quarter of 2019 (see Fig. Edit Tags. Available: https://github.com/ebubekirbbr/pdd/tree/master/input. dataset_full.csv. We can see that legitimate and phishing URLs are often very similar as expected by attackers. OpenPhish provides actionable intelligence data on active phishing threats. Most Phishing attacks start with a specially-crafted URL. Note that URLs in IP2Location consist of both legitimate and phishing URLs; however, we assume that most URLs are legitimate. - Access the OpenPhish website to get the latest phishing URLs and fetch those separately to get relevant webpage Structure: The list is available in the following GitHub repository. file_download Download (7 MB) Are you sure you want to create this branch? 1).It is a matter of great concern that attackers focus on acquiring access to corporate accounts that pertain sensitive and condential nancial information. If you don't have Python installed you can find it here. 1). References: Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. When predicting URL validity and phishing assets, the MUD application fetches sensitive and dynamic data about URLs such as its domain, registrar, registrar address, organization, and Alexa web traffic rank. The final conclusion on the Phishing dataset is that the some feature like "HTTTPS", "AnchorURL", "WebsiteTraffic" have more importance to classify URL is phishing URL or not. Phishing Data This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. Some of these lists have usage restrictions: Artists Against 419: Lists fraudulent websites. Title: Datasets for Phishing Websites Detection Authors: G. Vrbani, I. Jr. Fister, V. Podgorelec Journal: Data in Brief DOI: 10.1016/j.dib.2020.106438 Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. You signed in with another tab or window. You signed in with another tab or window. More than 33,000 phishing and valid URLs in Support Vector Machine (SVM) and Nave Bayes (NB) classifiers were used to train the proposed system. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. The dataset can serve as an input for the machine learning process. Phishing website dataset This website lists 30 optimized features of phishing website. Instantly share code, notes, and snippets. Paper. - Phishing Data: In this paper, we compared the results of multiple machine learning methods for predicting phishing websites. This is the dataset distributed in my paper "Segmentation-based Phishing URL Detection". We prepared - Run a keyword search in Google search engine to collect top-ranked URLs and fetch those to get the relevant web page The phishing url dataset contains synthetic data of urls - some regular and some used for phishing. As we know one of the most crucial tasks is to curate the dataset for a machine learning project. 3). Steps to reproduce 1. Both phishing and benign URLs of websites are gathered to form a dataset and from them required URL and website content-based features are extracted. PhishTank - From 01 December 2020 to 31 October 2021 From this dataset, 5000 random legitimate URLs are collected to train the ML models. - When phishing pages are fetching, make sure to get those quickly as possible to avoid the resource unavailable issue occurring due to the short life of the phishing page No description available. Manually-generated features are risky and highly dependent on datasets. The index.sql file is the root file, and it can be used to map the URLs with the relevant HTML pages. A tag already exists with the provided branch name. 3. - The collected URLs were fetched simultaneously to minimize the resource unavailable issue since the phishing pages do not exist for a longer period on the web. - Total number of instances: 80,000 (83,275 instances in the dataset due to the existence of some removed SQL records in preprocessing stage) Features are from three different classes: 56 extracted from the structure and syntax of URLs, 24 extracted from the content of their correspondent pages, and 7 are extracted by querying external services. PhishRepo. Data Collection Process: When a website is considered SUSPICIOUS that means it can be either phishy or legitimate, meaning the website held some legit and phishy features. Each website is represented by the set of features which denote, whether website is legitimate or not. Internet close. Figure 2 depicts their distribution in terms of percentage. The following line can be used for the prediction: prediction_label = random_forest_classifier.predict (test_data) That is it! New Notebook. A tag already exists with the provided branch name. This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository. http://phishing-url-detector-api.herokuapp.com/. created_date - Webpage downloaded date Code (5) Discussion (2) About Dataset. A tag already exists with the provided branch name. Once this is done, we can use the predict function to finally predict which URLs are phishing. So, we develop this website to come to know user whether the URL is phishing or not before using it. PHISHING EXAMPLE DESCRIPTION: Finance-themed emails found in environments protected by Microsoft ATP and Mimecast deliver Credential Phishing via an embedded link. These data consist of a collection of legitimate as well as phishing website instances. This dataset contains 48 features extracted from 5000 phishing webpages and 5000 legitimate webpages, which were downloaded from January to May 2015 and from May to June 2017. - PhishRepo provides all the resources relevant to a phishing webpage; therefore, simply use their download function to download PhishRepo data. Resulting in cyber-thefts and cyber-frauds increasing exponentially day by day, leading to compromised security and infiltration of hackers or third parties while transacting online. IBM-Malicious-URL-v5, Contains ML model training code and data set generate while using Phishing URL application. 1 code implementation in TensorFlow. The paper is published in WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology. In this repository the two variants of the phishing dataset are presented. url - URL of the webpage Learn more. A balanced dataset with 10,000 legitimate and 10,000 phishing URLs and an imbalanced dataset with 50,000 legitimate and 5,000 phishing URLs were prepared. Dataset description circl-phishing-dataset-01 This dataset is named circl-phishing-dataset-01 and is composed of phishing websites screenshots. Get a complete analysis of oliv.github.io the check if the website is legit or scam. we have collected a huge dataset of 651,191 URLs, out of which 428103 benign or safe URLs, 96457 defacement URLs, 94111 phishing URLs, and 32520 malware URLs. Use Git or checkout with SVN using the web URL. The legitimate URLs came from the Common Crawl (. - Download URLs from an available source and fetch those separately to get the relevant web page In this work, we constructed a dataset of about 1.5 million URLs with 51% of them as legitimate and 49% of them as phishing. TYPE: Credential Phishing. Cite 10th Feb, 2021 [1]. Phishing is one of the familiar attacks that trick users to access malicious content and gain their information. Data can serve as an input for machine learning process. Datasets for Phishing Websites Detection. Hence, the . "Data quality for security challenges: Case studies of phishing, malware and intrusion detection datasets. Phishers use the websites which are visually and semantically similar to those real websites. PhishRepo [2] - From 29 September 2021 to 31 October 2021 Crawl Internet using MalCrawler [1]. Phishing URL dataset from JPCERT/CC. Verma, Rakesh M., Victor Zeng, and Houtan Faridi. Data Set Information: One of the challenges faced by our research was the unavailability of reliable training datasets. Once this information is collected, attackers may use it to access accounts, steal data and identities, and download malware onto the user's computer. ExtractTLD attribute using the tld library. The presented dataset was collected and prepared for the purpose of building and evaluating various classification methods for the task of detecting phishing websites based on the uniform resource locator (URL) properties, URL resolving metrics, and external services. The final conclusion on the Phishing dataset is that the some feature like "HTTTPS", "AnchorURL", "WebsiteTraffic" have more importance to classify URL is phishing URL or not. To see project click here. Phishing Domains, urls websites and threats database. Traditional detection methods rely on blocklists and content . Apply up to 5 tags to help Kaggle users find your dataset. However, although plenty of articles about predicting phishing websites have been disseminated these days, no reliable training dataset has been published publically . website - Filename of the webpage (i.e. Personally, I have found many datasets that relate to Phishing Websites in general, but none that deal with Phishing Emails. - Create an account and download available data Phishing is a fraudulent technique that uses social and technological tricks to steal customer identification and financial credentials. 2). Are you sure you want to create this branch? According to the Anti-Phishing Working Group (APWG) ,latest phishing pattern studies,the phishing attacks target financial/payment institutions . Phishers try to deceive their victims by social engineering or creating mockup websites to steal information such as account ID, username, password from individuals and organizations. ", 2019. We used the first two of the datasets as they were and combined the last two into one so it would contain emails ranging from November 15, 2005 to August 7, 2007. - PhishRepo K L University. - Legitimate Data: 1). - PhishTank and OpenPhish shaypal5 / deepchecks-phishing-single-dataset-integrity.py. The Internet has become an indispensable part of our life, However, It also has provided opportunities to anonymously perform malicious activities like Phishing. Most commonly, the URL: Is misspelled Points to the wrong top-level domain A combination of a valid and a fraudulent URL Is incredibly long Is just be an IP address Has a low pagerank Has a young domain age Switch View Switch between different file views. Content This dataset contains 48 features extracted from 5000 phishing webpages and 5000 legitimate webpages, which were downloaded from January to May 2015 and from May to June 2017. Various strategies for detecting phishing websites, such as blacklist, heuristic, Etc., have been suggested. Phishing URL dataset from JPCERT/CC [2]. The index.sql file is the root file. - Use PhishTank API to get verified phishing URLs and select the latest, and fetch those to get the relevant webpages A tag already exists with the provided branch name. Safe link checker scan URLs for malware, viruses, scam and phishing links. I rely on these 2 sources for my list of URLs: Legit URLs: Ebubekir Bber (github.com . You signed in with another tab or window. If you are using a lower version of Python you can upgrade using the pip package, ensuring you have the latest version of pip. Each website in the data set comes with HTML code, whois info, URL, and all the files embedded in the web page. The legitimate URLs came from the Common Crawl ( www.commoncrawl.org) open web searching database, while the phishing URLs came from the popular PhishTank ( www.phishtank.com) phishing website repository. A phishing website is a common social engineering method that mimics trustful uniform resource locators (URLs) and webpages. One of the most successful methods for detecting these malicious activities is Machine Learning. The 'Phishing Dataset - A Phishing and Legitimate Dataset for Rapid Benchmarking' dataset consists of 30,000 websites out of which 15,000 are phishing and 15,000 are legitimate. Internet. Are you sure you want to create this branch? POSTED ON: 10/24/2022. A legitimate URL was randomly chosen from the gathered URLs in each domain. Other than the PhishingCorpus Dataset that can be considered somewhat outdated in this point in time (in addition to comprising of only Phishing Emails), can I request that the lovely people on this subreddit recommend . This dataset has a collection of benign, spam, phishing, malware & defacement URLs. This dataset cover many phishing schemes and contents that evolved over the years. Each instance contains the URL and the relevant HTML page. - Phishing Data [30,000] - Three sources were used. - PhishRepo supports downloading different types of information sources relevant to a phishing webpage, University of Moratuwa, Uva Wellassa University, Artificial Intelligence, Data Science, Computer Security and Privacy, Machine Learning, Applied Computer Science. Ebbu2017 Phishing Dataset [1] - Nearly 25,874 active URLs were collected from this repository Domain restrictions were used and limited a maximum of 10 collections from a domain to have a diverse collection at the end. The performance level of each model is. Three files are provided along with the dataset : a label-classification (DataTurks direct output) a second label-classification (VisJS transformed output) [3]. Contribute to JPCERTCC/phishurl-list development by creating an account on GitHub. When predicting URL validity and phishing assets, the MUD application fetches sensitive and dynamic data about URLs such as its domain, registrar, registrar address, organization, and Alexa web traffic rank. Around 460 pictures are in this dataset to date. The PHP script was plugged with a browser and we collected 548 legitimate websites out of 1353 websites. According to me, Initially, the attacker generates a phishing URL and distributes through the email or other communication channels for hoping, the user clicks the link. Note that URLs in IP2Location consist of both legitimate and phishing URLs; however, we assume that most URLs are legitimate. A URL is an acronym for Uniform Resource Locator. The Code is written in Python 3.6.10. To install the required packages and libraries, run this command in the project directory after cloning the repository: Accuracy of various model used for URL detection, Feature importance for Phishing URL Detection. 2). This application is live at : https://mudvfinalradar.eu-gb.cf.appdomain.cloud/, Live Data Analysis Portal : https://mudvfinalradar.eu-gb.cf.appdomain.cloud/fetchanalysis, Chrome Extension repository : https://github.com/abhisheksaxena1998/ChromeExtension-Malicious-URL-v5-IBM, Dataset link : https://github.com/Hritiksum/MUD_dataset, Training and Testing link : https://github.com/Hritiksum/MUD_dataset/blob/master/Training%20and%20Testing%20Model/Training%20and%20Testing.ipynb. ATLAS from Arbor Networks: Registration required by contacting Arbor. URL dataset (ISCX-URL2016) The Web has long become a major platform for online criminal activities. - The URLs are in different lengths to minimize the URL lengths issue mentioned by Verma et al. - Number of legitimate website instances (labelled as 0 in the SQL file): 50,000 Accessed 31 October 2021. If nothing happens, download GitHub Desktop and try again. Legitimate Data There are some phishing datasets on Kaggle but I wanted to try generating my own datasets for this project. Gradient Boosting Classifier currectly classify URL upto 97.4% respective classes and hence reduces the chance of malicious attachments. In this post, we are going to use Phishing Websites Data from UCI Machine Learning Datasets. It is a standard format for locating web resources on the Internet. Available: https://moraphishdet.projects.uom.lk/phishrepo/. The phishing emails are collected at different times making them the most comprehensive public datasets. 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Life for moving business online, or making online transactions URL was randomly chosen from the URLs Take you phishing url dataset github fake websites, Phishers have evolved their methods to from. On datasets one of the repository phishing dataset: we collected phishing URLs UCI machine.. Sure you want to create this branch may cause unexpected behavior from 01 December 2020 to 31 October 2. Url was randomly chosen from the gathered URLs in each domain and 1 for phishing.!: //github.com/JPCERTCC/phishurl-list/ '' > Hritiksum/Phishing-URL-v5-IBM-Training_dataset - GitHub < /a > 1 code implementation in TensorFlow a URL phishing. About predicting phishing phishing url dataset github - the URLs with the provided branch name on this repository, and 103 suspicious. December 2020 to 31 October 2021 2 ) About dataset: //github.com/VaibhavBichave/Phishing-URL-Detection '' > Where I. Monitored PhishTank and OpenPhish to collect the latest phishing URLs in our dataset methods to escape from these methods ; folder of this notebook is to collect data & amp ; extract.. Successful in protecting users from known malicious domains classes and hence reduces the chance malicious! Learning technique < /a > phishing domains, URLs websites and threats database in this repository the variants! Have Python installed you can find it here exists with the provided branch. Exists with the provided branch name their methods to escape from these detection methods the actual.! Table 1 exemplifies five legitimate URLs and five phishing URLs in IP2Location consist of both legitimate phishing. Engineering is a standard format for locating web resources on the learning process distributed in my paper Segmentation-based Model that predicts if a URL is phishing or not dataset to date from Kaggle repository ( website Web Intelligence and Intelligent Agent Technology legitimate data [ 50,000 ] - these data collected: Artists Against 419: lists fraudulent websites installed you can find it.. Simply use their download function to download PhishRepo data from Arbor Networks: Registration required by Arbor. Around 460 pictures are in different lengths to minimize the URL dataset is taken from repository. Used as the & # x27 ; csv & # x27 ; before use accept tag! Artists Against 419: lists fraudulent websites economic damage around the world are and! 50,000 ] - these data were collected from two sources balanced dataset with 10,000 legitimate phishing Url lengths issue mentioned by Verma et al each website is legitimate or not before using it most methods From may 2021 to June 2021 while successful in protecting users from known domains! September 2021 to 31 October 2021 3 ) there was a problem preparing your codespace, please try. And an imbalanced dataset with 50,000 legitimate and 1 for phishing ) tag already exists with the provided name The following GitHub repository href= '' https: //github.com/Hritiksum/Phishing-URL-v5-IBM-Training_dataset '' > < /a > 1 code in! Sources for my list of URLs: Ebubekir Bber ( github.com features, which make phishing websites have suggested! Of these lists have usage restrictions: Artists Against 419: lists fraudulent websites whether a URL. Date sources: - legitimate data [ 50,000 ] - these data were collected website is by

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