According to Cognitive Market Research, The Global Anti crawling Techniques market size is USD XX million in 2023 and will expand at a compound annual growth rate (CAGR) of 6.00% from 2023 to 2030.

North America Anti crawling Techniques held the major market of more than 40% of the global revenue and will grow at a compound annual growth rate (CAGR) of 4.2% from 2023 to 2030.
Europe Anti crawling Techniques accounted for a share of over 30% of the global market and are projected to expand at a compound annual growth rate (CAGR) of 4.5% from 2023 to 2030.
Asia Pacific Anti crawling Techniques held the market of more than 23% of the global revenue and will grow at a compound annual growth rate (CAGR) of 8.0% from 2023 to 2030.
South American Anti crawling Techniques market of more than 5% of the global revenue and will grow at a compound annual growth rate (CAGR) of 5.4% from 2023 to 2030.
Middle East and Africa Anti crawling Techniques held the major market of more than 2% of the global revenue and will grow at a compound annual growth rate (CAGR) of 5.7% from 2023 to 2030.
The market for anti-crawling techniques has grown dramatically as a result of the increasing number of data breaches and public awareness of the need to protect sensitive data. 
Demand for bot fingerprint databases remains higher in the anti crawling techniques market.
The content protection category held the highest anti crawling techniques market revenue share in 2023.

Increasing Demand for Protection and Security of Online Data to Provide Viable Market Output

The market for anti-crawling techniques is expanding due in large part to the growing requirement for online data security and protection. Due to an increase in digital activity, organizations are processing and storing enormous volumes of sensitive data online. Organizations are being forced to invest in strong anti-crawling techniques due to the growing threat of data breaches, illegal access, and web scraping occurrences. By protecting online data from harmful activity and guaranteeing its confidentiality and integrity, these technologies advance the industry. Moreover, the significance of protecting digital assets is increased by the widespread use of the Internet for e-commerce, financial transactions, and sensitive data transfers. Anti-crawling techniques are essential for reducing the hazards connected to online scraping, which is a tactic often used by hackers to obtain important data.

Increasing Incidence of Cyber Threats to Propel Market Growth

The growing prevalence of cyber risks, such as site scraping and data harvesting, is driving growth in the market for anti-crawling techniques. Organizations that rely significantly on digital platforms run a higher risk of having illicit data extracted. In order to safeguard sensitive data and preserve the integrity of digital assets, organizations have been forced to invest in sophisticated anti-crawling techniques that strengthen online defenses. Moreover, the market's growth is a reflection of growing awareness of cybersecurity issues and the need to put effective defenses in place against changing cyber threats. Moreover, cybersecurity is constantly challenged by the spread of advanced and automated crawling programs. The ever-changing threat landscape forces enterprises to implement anti-crawling techniques, which use a variety of tools like rate limitation, IP blocking, and CAPTCHAs to prevent fraudulent scraping efforts.

Market Restraints of the Anti crawling Techniques

Increasing Demand for Ethical Web Scraping to Restrict Market Growth

The growing desire for ethical web scraping presents a unique challenge to the anti-crawling techniques market. Ethical web scraping is the process of obtaining data from websites for lawful objectives, such as market research or data analysis, but without breaching the terms of service. Furthermore, the restraint arises because anti-crawling techniques must distinguish between criminal and ethical scraping operations, finding a balance between preventing websites from misuse and permitting authorized data harvest. This dynamic calls for more complex and adaptable anti-crawling techniques to distinguish between destructive and ethical scrapping actions.

Impact of COVID-19 on the Anti Crawling Techniques Market

The demand for online material has increased as a result of the COVID-19 pandemic, which has...

TakaraSpider Japanese Web Crawl Dataset

  Dataset Summary

TakaraSpider is a large-scale web crawl dataset specifically designed to capture Japanese web content alongside international sources. The dataset contains 257,900 web pages collected through systematic crawling, with a primary focus on Japanese language content (78.5%) while maintaining substantial international representation (21.5%). This makes it ideal for Japanese-English comparative studies, cross-cultural web… See the full description on the dataset page: https://huggingface.co/datasets/takarajordan/takaraspider.

By the end of 2023, ** percent of the top most used news websites in Germany were blocking Google's AI crawler, having been quick to act after the crawlers were launched. The figure was substantially lower in Spain and Poland, and in both cases, news publishers were slower to react, meaning that by the end of 2023 just ***** percent of top news sites (print, broadcast, and digital-born) in each country were blocking Google's AI from crawling their content.

Context

Common Crawl project has fascinated me ever since I learned about it. It provides a large number of data formats and presents challenges across skill and interest areas. I am particularly interested in URL analysis for applications such as typosquatting, malicious URLs, and just about anything interesting that can be done with domain names.

Content

I have sampled 1% of the domains from the Common Crawl Index dataset that is available on AWS in Parquet format. You can read more about how I extracted this dataset @ https://harshsinghal.dev/create-a-url-dataset-for-nlp/

Acknowledgements

Thanks a ton to the folks at https://commoncrawl.org/ for making this immensely valuable resource available to the world for free. Please find their Terms of Use here.

Inspiration

My interests are in working with string similarity functions and I continue to find scalable ways of doing this. I wrote about using a Postgres extension to compute string distances and used Common Crawl URL domains as the input dataset (you can read more @ https://harshsinghal.dev/postgres-text-similarity-with-commoncrawl-domains/).

I am also interested in identifying fraudulent domains and understanding malicious URL patterns.

This dataset is the result of a full-population crawl of the .gov.uk web domain, aiming to capture a full picture of the scope of public-facing government activity online and the links between different government bodies. Local governments have been developing online services, aiming to better serve the public and reduce administrative costs. However, the impact of this work, and the links between governments’ online and offline activities, remain uncertain. The overall research question for this research examines whether local e-government has met these expectations, of Digital Era Governance and of its practitioners. Aim was to directly analyse the structure and content of government online. It shows that recent digital-centric public administration theories, typified by the Digital Era Governance quasi-paradigm, are not empirically supported by the UK local government experience. The data consist of a file of individual Uniform Resource Locators (URLs) fetched during the crawl, and a further file containing pairs of URLs reflecting the Hypertext Markup Language (HTML) links between them. In addition, a GraphML format file is presented for a version of the data reduced to third-level-domains, with accompanying attribute data for the publishing government organisations and calculated webometric statistics based on the third-level-domain link network.This project engages with the Digital Era Governance (DEG) work of Dunleavy et. al. and draws upon new empirical methods to explore local government and its use of Internet-related technology. It challenges the existing literature, arguing that e-government benefits have been oversold, particularly for transactional services; it updates DEG with insights from local government. The distinctive methodological approach is to use full-population datasets and large-scale web data to provide an empirical foundation for theoretical development, and to test existing theorists’ claims. A new full-population web crawl of .gov.uk is used to analyse the shape and structure of online government using webometrics. Tools from computer science, such as automated classification, are used to enrich our understanding of the dataset. A new full-population panel dataset is constructed covering council performance, cost, web quality, and satisfaction. The local government web shows a wide scope of provision but only limited evidence in support of the existing rhetorics of Internet-enabled service delivery. In addition, no evidence is found of a link between web development and performance, cost, or satisfaction. DEG is challenged and developed in light of these findings. The project adds value by developing new methods for the use of big data in public administration, by empirically challenging long-held assumptions on the value of the web for government, and by building a foundation of knowledge about local government online to be built on by further research. This is an ESRC-funded DPhil research project. A web crawl was carried out with Heritrix, the Internet Archive's web crawler. A list of all registered domains in .gov.uk (and their www.x.gov.uk equivalents) was used as a set of start seeds. Sites outside .gov.uk were excluded; robots.txt files were respected, with the consequence that some .gov.uk sites (and some parts of other .gov.uk sites) were not fetched. Certain other areas were manually excluded, particularly crawling traps (e.g. calendars which will serve infinite numbers of pages in the past and future and those websites returning different URLs for each browser session) and the contents of certain large peripheral databases such as online local authority library catalogues. A full set of regular expressions used to filter the URLs fetched are included in the archive. On completion of the crawl, the page URLs and link data were extracted from the output WARC files. The page URLs were manually examined and re-filtered to handle various broken web servers and to reduce duplication of content where multiple views were presented onto the same content (for example, where a site was presented at both http://organisation.gov.uk/ and http://www.organisation.gov.uk/ without HTTP redirection between the two). Finally, The link list was filtered against the URL list to remove bogus links and both lists were map/reduced to a single set of files. Also included in this data release is a derived dataset more useful for high-level work. This is a GraphML file containing all the link and page information reduced to third-level domain level (so darlington.gov.uk is considered as a single node, not a large set of pages) and with the links binarised to present/not present between each node. Each graph node also has various attributes, including the name of the registering organisation and various webometric measures including PageRank, indegree and betweenness centrality.

In the eyes of French SEOs, if there was one point that mattered absolutely in terms of SEO for mobile first indexing, it was the adaptation of the size of the content to the size of the screen in 2020. Other than that, when crawl was ensured, it made it easier for the crawler or the Internet user to visit and to facilitate the discovery of a site by search engines.

CrawlEval

Resources and tools for evaluating the performance and behavior of web crawling systems.

  Overview

CrawlEval provides a comprehensive suite of tools and datasets for evaluating web crawling systems, with a particular focus on HTML pattern extraction and content analysis. The project includes:

A curated dataset of web pages with ground truth patterns Tools for fetching and analyzing web content Evaluation metrics and benchmarking capabilities

  Dataset… See the full description on the dataset page: https://huggingface.co/datasets/crawlab/crawleval.

Dataset

This dataset was created by Josh Ko

Contents

Anti Crawling Techniques Market Outlook

In 2023, the global anti-crawling techniques market size was valued at approximately USD 2.1 billion, with projections suggesting it will reach around USD 5.3 billion by 2032, exhibiting a CAGR of 10.8% over the forecast period. The market is primarily driven by the increasing need to protect sensitive data and secure web platforms against malicious scraping activities, which has become more critical with the growth of digital transformation across various industries.

The surge in e-commerce activities and the proliferation of online platforms have significantly contributed to the growth of the anti-crawling techniques market. As companies increasingly rely on online presence to drive their business, the need to protect their web content from scraping and unauthorized access has become paramount. E-commerce giants and smaller online retailers alike are investing heavily in anti-crawling solutions to safeguard their competitive edge and ensure that pricing, product information, and customer data are not compromised by malicious bots.

Another crucial growth factor is the increasing incidence of cyber threats and data breaches. With cybercriminals employing sophisticated crawling techniques to collect valuable information, organizations are compelled to adopt advanced anti-crawling measures. The financial services sector, in particular, faces significant risks due to the sensitive nature of the data they handle. The adoption of anti-crawling techniques in this sector is driven by regulatory requirements and the necessity to protect customer data from being harvested by malicious entities.

Technological advancements and the development of innovative anti-crawling solutions are also accelerating market growth. The integration of machine learning and artificial intelligence into anti-crawling techniques has enhanced the ability to detect and mitigate sophisticated crawling activities. Companies are leveraging these advanced technologies to stay ahead of cyber threats and ensure robust security for their web assets. Furthermore, the increasing availability of cloud-based anti-crawling solutions has made it easier for organizations of all sizes to deploy and manage these security measures efficiently.

Regionally, North America holds the largest share of the anti-crawling techniques market, driven by the presence of major technology companies and a strong focus on cybersecurity. Europe follows closely, with stringent data protection regulations such as the GDPR propelling the adoption of anti-crawling solutions. The Asia Pacific region is expected to witness the highest growth rate due to rapid digitalization and increasing internet penetration. Latin America and the Middle East & Africa are also experiencing growing demand for anti-crawling techniques, albeit at a slower pace compared to other regions.

Technique Type Analysis

IP Blocking is one of the most widely used anti-crawling techniques. By identifying and blocking IP addresses associated with malicious bot activities, organizations can effectively prevent unauthorized crawling. This method is particularly effective in scenarios where the source of the crawling activity is consistent and predictable. However, it may not be as effective against sophisticated bots that use rotating IP addresses or proxy servers. Despite this limitation, IP Blocking remains a critical component of many organizations' anti-crawling strategies, especially when combined with other techniques.

User-Agent Blocking involves filtering out requests from known bot user agents. Every web request includes a user-agent string that identifies the browser or tool making the request. By maintaining a blacklist of user agents associated with crawlers, organizations can block these requests at the server level. However, advanced bots can spoof user-agent strings to mimic legitimate traffic, making this technique less effective on its own. Nevertheless, User-Agent Blocking is a valuable first line of defense in a multi-layered anti-crawling strategy.

CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) is another widely adopted anti-crawling technique. By requiring users to complete a challenge that is easy for humans but difficult for bots, CAPTCHA can effectively distinguish between legitimate users and automated scripts. This technique is particularly useful for preventing automated form submissions and account creation. However, it can also introduce friction for legitimate users, potentially impacting user experience. Therefore

This object has been created as a part of the web harvesting project of the Eötvös Loránd University Department of Digital Humanities ELTE DH. Learn more about the workflow HERE about the software used HERE.The aim of the project is to make online news articles and their metadata suitable for research purposes. The archiving workflow is designed to prevent modification or manipulation of the downloaded content. The current version of the curated content with normalized formatting in standard TEI XML format with Schema.org encoded metadata is available HERE. The detailed description of the raw content is the following:

• The portal's archived content (from 2000-09-10 to 2021-10-16) in WARC format available HERE (crawled: 2021-09-02T19:51:04.673039 - 2021-10-16T16:25:30.654675).

Dataset Card for "AI-paper-crawl"

The dataset contains 11 splits, corresponding to 11 conferences. For each split, there are several fields:

"index": Index number starting from 0. It's the primary key; "text": The content of the paper in pure text form. Newline is turned into 3 spaces if "-" is not detected; "year": A string of the paper's publication year, like "2018". Transform it into int if you need to; "No": A string of index number within a year. 1-indexed. In "ECCV" split… See the full description on the dataset page: https://huggingface.co/datasets/Seed42Lab/AI-paper-crawl.

The Collection Management Webpages (CMW) team is responsible for collecting, processing and storing webpages from different sources including tweets from multiple collections and contributors, such as those related to events and trends studied in local projects like IDEAL/GETAR, and webpage archives collected by Pranav Nakate, Mohamed Farag, and others. Thus, based on webpage sources, we divide our work into the three following deliverable and manageable tasks. The first task is to fetch the webpages mentioned in the tweets that are collected by the Collection Management Tweets (CMT) team. Those webpages are then stored in WARC files, processed, and loaded into HBase. The second task is to run focused crawls for all of the events mentioned in IDEAL/GETAR to collect relevant webpages. And similar to the first task, we would then store the webpages into WARC files, process them, and load them into HBase. We also plan to achieve the third task which is similar to the first two, except that the webpages are from archives collected by the people previously involved in the project. Since these tasks are time-consuming and sensitive to real-time processing requirements, it is essential that our approach be incremental, meaning that webpages need to be incrementally collected, processed, and stored to HBase. We have conducted multiple experiments for the first, second, and third tasks, on our local machines as well as the cluster. For the second task, we manually collected a number of seed URLs of events, namely “South China Sea Disputes”, “USA President Election 2016”, and “South Korean President Protest”, to train the focused event crawler, and then ran the trained model on a small number of URLs that are randomly generated as well as manually collected. Encouragingly, these experiments ran successfully; however, we still have to work to scale up the experimenting data to be systematically run on the cluster. The two main components to be further improved and tested are the HBase data connector and handler, and the focused event crawler. While focusing on our own tasks, the CMW team works closely with other teams whose inputs and outputs depend on our team. For example, the front-end (FE) team might use our results for their front-end content. We discussed with the Classification (CLA) team to have some agreements on filtering and noise reducing tasks. Also, we made sure that we would get the right format URLs from the Collection Management Tweets (CMT) team. In addition, the other two teams, Clustering and Topic Analysis (CTA) and SOLR, will use our team’s outputs for topic analyzing and indexing, respectively. For instance, based on the SOLR team’s requests and consensus, we have finalized a schema (i.e., specific fields of information) for a webpage to be collected and stored. In this final report, we report our CMW team’s overall results and progress. Essentially, this report is a revised version of our three interim reports based on Dr. Fox’s and peer-reviewers’ comments. Besides to this revising, we continue reporting our ongoing work, challenges, processes, evaluations, and plans. This submission includes the following files: 1- CS5604Fall2016_CMW_Report (in Word and PDF format): the final report describing the team's overall work and findings. 2- CS5604Fall2016_CMW_Presentation (in PowerPoint and PDF format): the final presentation the team presented before the class. 3- CS5604Fall2016_CMW_Software.zip contains scripts that: 3.1- fetch webpages in HTML and save them into WARC 3.2- save webpages into HBase 3.3- run event focus crawler (efc) to collect webpages 4- CS5604Fall2016_CMW_efcData.zip: contains data generated by the efc. NSF IIS-1319578 and 1619028

The crawl space encapsulation service market is experiencing significant growth, driven by increasing awareness of the benefits of improved indoor air quality, energy efficiency, and protection against moisture damage. The market size in 2025 is estimated at $1.312 billion, demonstrating substantial demand for these services. While the provided CAGR is missing, a reasonable estimate, considering the robust growth drivers, could be placed between 6% and 8% annually for the forecast period (2025-2033). This growth is fueled by several factors: rising concerns about mold and mildew in crawl spaces, stricter building codes promoting energy efficiency, and the increasing popularity of environmentally friendly encapsulation materials like plastic sheeting and concrete. The segmentation of the market into plastic-based and concrete-based solutions, as well as residential and commercial applications, provides further avenues for growth and specialization within the industry. Geographic expansion, particularly in regions with humid climates or older housing stock, represents another significant opportunity. Competition is relatively fragmented, with numerous regional and national companies vying for market share. Key players such as Lee Company, Perma Dry Waterproofing, and Basement Systems are establishing brand recognition and leveraging their expertise to capture a larger customer base. However, the market's fragmented nature also presents opportunities for new entrants to establish a niche. While challenges such as the initial cost of encapsulation and potential regional variations in demand exist, the overall market outlook for crawl space encapsulation services remains positive, promising continued expansion over the next decade.

Introduction

This comprehensive dataset provides essential details like websites, addresses, categories, and more. Gain valuable insights to:

Generate targeted B2B leads Fuel local SEO campaigns

US Yellow pages dataset with more than 23K+ records. This is small subset from the one of our large Yellow pages dataset.

Content

US Yellow pages sample dataset

Fields:

name phone_number website years_in_yp year_in_business crawled_at tags url _id track_mp address category thumbnail

Acknowledgments

Dataset crawled by crawl feeds.com in house team.

Five web-crawlers written in the R language for retrieving Slovenian texts from the news portals 24ur, Dnevnik, Finance, Rtvslo, and Žurnal24. These portals contain political, business, economic and financial content.

Anti-Crawling Techniques Market Analysis The global anti-crawling techniques market is anticipated to reach a valuation of USD 231 million by 2033, expanding at a CAGR of 10.3% from 2025 to 2033. This growth is driven by the increasing prevalence of malicious web crawling activities, such as web scraping, that can harm businesses by extracting sensitive data, abusing resources, or manipulating online prices. The market is segmented into applications such as content protection, price protection, and advertisement protection, and types including bot fingerprint databases, JavaScript tags, and cloud APIs. Key trends in the anti-crawling techniques market include the emergence of advanced technologies like intent-based deep behavior analysis (IDBA) and the growing emphasis on preventing sophisticated bot attacks. The market is dominated by established players such as Ziwit Enterprise, Radware, Imperva, and Paloalto, while regional markets in North America, Europe, Asia Pacific, and the Middle East & Africa present significant growth opportunities. The rising adoption of e-commerce and the increasing value of online data are expected to fuel further demand for anti-crawling solutions in the years to come.

Dataset

This dataset was created by Pranav Bathija

Contents

In the recent years, Transformer-based models have lead to significant advances in language modelling for natural language processing. However, they require a vast amount of data to be (pre-)trained and there is a lack of corpora in languages other than English. Recently, several initiatives have presented multilingual datasets obtained from automatic web crawling. However, the results in Spanish present important shortcomings, as they are either too small in comparison with other languages, or present a low quality derived from sub-optimal cleaning and deduplication. In this paper, we introduce esCorpius, a Spanish crawling corpus obtained from near 1 Pb of Common Crawl data. It is the most extensive corpus in Spanish with this level of quality in the extraction, purification and deduplication of web textual content. Our data curation process involves a novel highly parallel cleaning pipeline and encompasses a series of deduplication mechanisms that together ensure the integrity of both document and paragraph boundaries. Additionally, we maintain both the source web page URL and the WARC shard origin URL in order to complain with EU regulations. esCorpius has been released under CC BY-NC-ND 4.0 license.

Dataset

This dataset was created by Timo Bozsolik

Contents

Elevate your AI and machine learning projects with our comprehensive fashion image dataset, carefully curated to meet the needs of cutting-edge applications in e-commerce, product recommendation systems, and fashion trend analysis.

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