A study released in June 2025 that looked at about 82,000 websites found that Google was responsible for almost ** percent of the traffic generated to these domains. Direct traffic corresponded to around **** percent of the investigated websites' traffic volume. While traditional search engines like Bing and social networks like Facebook represented larger shares, ChatGPT overtook Reddit and LinkedIn with a slightly larger share, indicating an increase in traffic from these platforms.

Desktop and mobile website traffic data showed that Germany domain of Zalando had by far the highest number of visitors compared to all other European countries. Between July 2023 and December 2023, zalando.de recorded more nearly *** million visits. The Polish web domain followed in the ranking, as the total visits amounted to **** million.

As Americans are trying to keep up with current government guidelines and recommendations during the coronavirus pandemic, the Center for Disease Control and Prevention (CDC.gov) had almost 934 million pageviews in the preceding 30 days. The CDC is the most trusted source of information for the U.S. public regarding the current COVID-19 outbreak, followed by other government and public health websites.

Datos brings to market anonymized, at scale, consolidated privacy-secured datasets with a granularity rarely found in the market. Get access to the desktop and mobile browsing behavior for millions of users across the globe, packaged into clean, easy-to-understand data products and reports.

The Datos Domain Traffic Feed reports on panelist visitation to domains, benchmarking the popularity of internet properties worldwide by country. Additionally, we offer the ability to track the availability of domains with respect to whether traffic is being sent to sites which are currently unregistered. Customers can elect to focus on specific domains, countries, or domain registration status.

Now available with Datos Low-Latency Feed This add-on ensures delivery of approximately 99% of all devices before markets open in New York (the lowest latency product on the market). Our clickstream data is made up of an array of upstream sources. The DLLF makes the daily output of these sources available as they arrive and are processed, rather than a once-daily batch.

With Versium REACH's IP to Domain you unlock the ability to de-anonymize your database of IP addresses. Receive firmographic data for an IP address that includes up to 3 likely businesses, including key attributes such as domain, company size, location, and many other valuable firmographic insights.

DataForSEO Labs API offers three powerful keyword research algorithms and historical keyword data:

• Related Keywords from the “searches related to” element of Google SERP. • Keyword Suggestions that match the specified seed keyword with additional words before, after, or within the seed key phrase. • Keyword Ideas that fall into the same category as specified seed keywords. • Historical Search Volume with current cost-per-click, and competition values.

Based on in-market categories of Google Ads, you can get keyword ideas from the relevant Categories For Domain and discover relevant Keywords For Categories. You can also obtain Top Google Searches with AdWords and Bing Ads metrics, product categories, and Google SERP data.

You will find well-rounded ways to scout the competitors:

• Domain Whois Overview with ranking and traffic info from organic and paid search. • Ranked Keywords that any domain or URL has positions for in SERP. • SERP Competitors and the rankings they hold for the keywords you specify. • Competitors Domain with a full overview of its rankings and traffic from organic and paid search. • Domain Intersection keywords for which both specified domains rank within the same SERPs. • Subdomains for the target domain you specify along with the ranking distribution across organic and paid search. • Relevant Pages of the specified domain with rankings and traffic data. • Domain Rank Overview with ranking and traffic data from organic and paid search. • Historical Rank Overview with historical data on rankings and traffic of the specified domain from organic and paid search. • Page Intersection keywords for which the specified pages rank within the same SERP.

All DataForSEO Labs API endpoints function in the Live mode. This means you will be provided with the results in response right after sending the necessary parameters with a POST request.

The limit is 2000 API calls per minute, however, you can contact our support team if your project requires higher rates.

We offer well-rounded API documentation, GUI for API usage control, comprehensive client libraries for different programming languages, free sandbox API testing, ad hoc integration, and deployment support.

We have a pay-as-you-go pricing model. You simply add funds to your account and use them to get data. The account balance doesn't expire.

Domain Name Service Market Outlook

The global Domain Name Service (DNS) market size was valued at approximately USD 1.2 billion in 2023, and is projected to reach around USD 2.5 billion by 2032, exhibiting a CAGR of 8.3% during the forecast period. This remarkable growth can be attributed to the increasing need for efficient traffic management, enhanced network security, and the growing number of internet users globally. As businesses increasingly operate in the digital domain, the demand for reliable and scalable DNS solutions has never been higher. The surge in e-commerce, cloud computing, and online services further propels the requirement for advanced DNS solutions to ensure optimal performance and user experience.

One of the primary growth factors driving the DNS market is the exponential increase in internet traffic and the number of web-based applications. The proliferation of IoT devices and the expansion of cloud services have led to a substantial rise in the number of domain names being registered. This surge necessitates more sophisticated DNS management solutions that can handle large volumes of queries and ensure low-latency responses. Furthermore, the growing concerns around cybersecurity and the rising incidences of DDoS attacks have underscored the importance of DNS security, prompting organizations to invest in robust managed DNS services to safeguard their digital assets.

Another significant factor contributing to market growth is the growing trend of digital transformation across various industries. Companies across sectors such as healthcare, BFSI, retail, and IT are increasingly adopting digital strategies to enhance their operations and customer engagement. This digital shift requires reliable and efficient DNS solutions to manage the increased web traffic and ensure uninterrupted online services. The adoption of cloud-based DNS services is particularly prominent, driven by the benefits of scalability, flexibility, and cost-effectiveness that cloud solutions offer. Moreover, the increasing adoption of multi-cloud environments further boosts the demand for comprehensive DNS management solutions.

Technological advancements and innovations in DNS solutions also play a crucial role in driving market growth. The development of advanced DNS features such as DNSSEC (Domain Name System Security Extensions) to prevent data breaches and the integration of AI and machine learning for predictive analytics and automated traffic management are gaining traction. Additionally, the implementation of IPv6 and the continuous expansion of the internet infrastructure are expected to create new opportunities for DNS service providers. These technological strides not only enhance the functionality and security of DNS solutions but also cater to the evolving needs of modern enterprises.

Regionally, North America holds a significant share of the DNS market, driven by the presence of major DNS service providers and the high adoption rate of advanced technologies. The region's well-established IT infrastructure and the increasing focus on cybersecurity further propel the demand for DNS solutions. Meanwhile, the Asia Pacific region is expected to witness the highest growth rate during the forecast period. Rapid digitalization, the growing internet user base, and the expansion of e-commerce in countries like China and India are key factors contributing to this growth. Europe also presents substantial opportunities, with increasing investments in IT infrastructure and the rising adoption of cloud services.

Type Analysis

The Domain Name Service market can be segmented based on type into Managed DNS and Unmanaged DNS. Managed DNS services are gaining substantial traction due to their ability to provide enhanced control, security, and performance. These services are particularly beneficial for businesses with complex and large-scale DNS requirements, offering features such as traffic load balancing, failover support, and advanced security protocols. The growing need to mitigate cyber threats and ensure uninterrupted online services is driving organizations to opt for managed DNS solutions, which are often provided by third-party vendors with expertise in DNS management.

Unmanaged DNS services, on the other hand, are typically used by smaller enterprises or individual users who require basic DNS functionalities without the need for advanced features. These services offer a cost-effective solution for managing domain n

Explore the historical Whois records related to free-web-traffic-report.com (Domain). Get insights into ownership history and changes over time.

Dataset contains generated traffic from single requests towards DNS and DNS over Encryption servers as well as network traffic generated by browsers towards multiple DNS over HTTPS servers. The dataset contains also logs and csv files with queried domains. The IP addresses of the DoH servers are provided in the readme so that users can easily label the data extracted from pcap files. The dataset may be used for Machine Learning purposes (DNS over HTTPS identification).

Explore the historical Whois records related to instinctive-web-traffic.com (Domain). Get insights into ownership history and changes over time.

Please refer to the original data article for further data description: Jan Luxemburk et al. CESNET-QUIC22: A large one-month QUIC network traffic dataset from backbone lines, Data in Brief, 2023, 108888, ISSN 2352-3409, https://doi.org/10.1016/j.dib.2023.108888. We recommend using the CESNET DataZoo python library, which facilitates the work with large network traffic datasets. More information about the DataZoo project can be found in the GitHub repository https://github.com/CESNET/cesnet-datazoo. The QUIC (Quick UDP Internet Connection) protocol has the potential to replace TLS over TCP, which is the standard choice for reliable and secure Internet communication. Due to its design that makes the inspection of QUIC handshakes challenging and its usage in HTTP/3, there is an increasing demand for research in QUIC traffic analysis. This dataset contains one month of QUIC traffic collected in an ISP backbone network, which connects 500 large institutions and serves around half a million people. The data are delivered as enriched flows that can be useful for various network monitoring tasks. The provided server names and packet-level information allow research in the encrypted traffic classification area. Moreover, included QUIC versions and user agents (smartphone, web browser, and operating system identifiers) provide information for large-scale QUIC deployment studies. Data capture The data was captured in the flow monitoring infrastructure of the CESNET2 network. The capturing was done for four weeks between 31.10.2022 and 27.11.2022. The following list provides per-week flow count, capture period, and uncompressed size:

W-2022-44

Uncompressed Size: 19 GB Capture Period: 31.10.2022 - 6.11.2022 Number of flows: 32.6M W-2022-45

Uncompressed Size: 25 GB Capture Period: 7.11.2022 - 13.11.2022 Number of flows: 42.6M W-2022-46

Uncompressed Size: 20 GB Capture Period: 14.11.2022 - 20.11.2022 Number of flows: 33.7M W-2022-47

Uncompressed Size: 25 GB Capture Period: 21.11.2022 - 27.11.2022 Number of flows: 44.1M CESNET-QUIC22

Uncompressed Size: 89 GB Capture Period: 31.10.2022 - 27.11.2022 Number of flows: 153M

Data description The dataset consists of network flows describing encrypted QUIC communications. Flows were created using ipfixprobe flow exporter and are extended with packet metadata sequences, packet histograms, and with fields extracted from the QUIC Initial Packet, which is the first packet of the QUIC connection handshake. The extracted handshake fields are the Server Name Indication (SNI) domain, the used version of the QUIC protocol, and the user agent string that is available in a subset of QUIC communications. Packet Sequences Flows in the dataset are extended with sequences of packet sizes, directions, and inter-packet times. For the packet sizes, we consider payload size after transport headers (UDP headers for the QUIC case). Packet directions are encoded as ±1, +1 meaning a packet sent from client to server, and -1 a packet from server to client. Inter-packet times depend on the location of communicating hosts, their distance, and on the network conditions on the path. However, it is still possible to extract relevant information that correlates with user interactions and, for example, with the time required for an API/server/database to process the received data and generate the response to be sent in the next packet. Packet metadata sequences have a length of 30, which is the default setting of the used flow exporter. We also derive three fields from each packet sequence: its length, time duration, and the number of roundtrips. The roundtrips are counted as the number of changes in the communication direction (from packet directions data); in other words, each client request and server response pair counts as one roundtrip. Flow statistics Flows also include standard flow statistics, which represent aggregated information about the entire bidirectional flow. The fields are: the number of transmitted bytes and packets in both directions, the duration of flow, and packet histograms. Packet histograms include binned counts of packet sizes and inter-packet times of the entire flow in both directions (more information in the PHISTS plugin documentation There are eight bins with a logarithmic scale; the intervals are 0-15, 16-31, 32-63, 64-127, 128-255, 256-511, 512-1024, >1024 [ms or B]. The units are milliseconds for inter-packet times and bytes for packet sizes. Moreover, each flow has its end reason - either it was idle, reached the active timeout, or ended due to other reasons. This corresponds with the official IANA IPFIX-specified values. The FLOW_ENDREASON_OTHER field represents the forced end and lack of resources reasons. The end of flow detected reason is not considered because it is not relevant for UDP connections. Dataset structure The dataset flows are delivered in compressed CSV files. CSV files contain one flow per row; data columns are summarized in the provided list below. For each flow data file, there is a JSON file with the number of saved and seen (before sampling) flows per service and total counts of all received (observed on the CESNET2 network), service (belonging to one of the dataset's services), and saved (provided in the dataset) flows. There is also the stats-week.json file aggregating flow counts of a whole week and the stats-dataset.json file aggregating flow counts for the entire dataset. Flow counts before sampling can be used to compute sampling ratios of individual services and to resample the dataset back to the original service distribution. Moreover, various dataset statistics, such as feature distributions and value counts of QUIC versions and user agents, are provided in the dataset-statistics folder. The mapping between services and service providers is provided in the servicemap.csv file, which also includes SNI domains used for ground truth labeling. The following list describes flow data fields in CSV files:

ID: Unique identifier SRC_IP: Source IP address DST_IP: Destination IP address DST_ASN: Destination Autonomous System number SRC_PORT: Source port DST_PORT: Destination port PROTOCOL: Transport protocol QUIC_VERSION QUIC: protocol version QUIC_SNI: Server Name Indication domain QUIC_USER_AGENT: User agent string, if available in the QUIC Initial Packet TIME_FIRST: Timestamp of the first packet in format YYYY-MM-DDTHH-MM-SS.ffffff TIME_LAST: Timestamp of the last packet in format YYYY-MM-DDTHH-MM-SS.ffffff DURATION: Duration of the flow in seconds BYTES: Number of transmitted bytes from client to server BYTES_REV: Number of transmitted bytes from server to client PACKETS: Number of packets transmitted from client to server PACKETS_REV: Number of packets transmitted from server to client PPI: Packet metadata sequence in the format: [[inter-packet times], [packet directions], [packet sizes]] PPI_LEN: Number of packets in the PPI sequence PPI_DURATION: Duration of the PPI sequence in seconds PPI_ROUNDTRIPS: Number of roundtrips in the PPI sequence PHIST_SRC_SIZES: Histogram of packet sizes from client to server PHIST_DST_SIZES: Histogram of packet sizes from server to client PHIST_SRC_IPT: Histogram of inter-packet times from client to server PHIST_DST_IPT: Histogram of inter-packet times from server to client APP: Web service label CATEGORY: Service category FLOW_ENDREASON_IDLE: Flow was terminated because it was idle FLOW_ENDREASON_ACTIVE: Flow was terminated because it reached the active timeout FLOW_ENDREASON_OTHER: Flow was terminated for other reasons

Link to other CESNET datasets

https://www.liberouter.org/technology-v2/tools-services-datasets/datasets/ https://github.com/CESNET/cesnet-datazoo Please cite the original data article:

@article{CESNETQUIC22, author = {Jan Luxemburk and Karel Hynek and Tomáš Čejka and Andrej Lukačovič and Pavel Šiška}, title = {CESNET-QUIC22: a large one-month QUIC network traffic dataset from backbone lines}, journal = {Data in Brief}, pages = {108888}, year = {2023}, issn = {2352-3409}, doi = {https://doi.org/10.1016/j.dib.2023.108888}, url = {https://www.sciencedirect.com/science/article/pii/S2352340923000069} }

Explore the historical Whois records related to website-traffic-ads.com (Domain). Get insights into ownership history and changes over time.

This dataset provides a list of the top one million domains as monitored by Cloudflare Radar for the week of February 24 to March 3, 2025. The data is useful for analyzing internet trends, domain popularity, and web traffic insights.

Explore the historical Whois records related to uk-web-traffic.com (Domain). Get insights into ownership history and changes over time.

shopify.com is ranked #82 in US with 245.63M Traffic. Categories: Computer Software and Development, Online Services. Learn more about website traffic, market share, and more!

This is a simple dataset which groups the administrative network traffic volume from OpenStack clouds by Virtual Machine (VM) operations. This dataset considers ten different images of OS for the VMs.

The dataset contains DNS records, IP-related features, WHOIS/RDAP information, information from TLS handshakes and certificates, and GeoIP information for 368,956 benign domains from Cisco Umbrella, 461,338 benign domains from the actual CESNET network traffic, 164,425 phishing domains from PhishTank and OpenPhish services, and 100,809 malware domains from various sources like ThreatFox, The Firebog, MISP threat intelligence platform, and other sources. The ground truth for the phishing dataset was double-check with the VirusTotal (VT) service. Domain names not considered malicious by VT have been removed from phishing and malware datasets. Similarly, benign domain names that were considered risky by VT have been removed from the benign datasets. The data was collected between March 2023 and July 2024. The final assessment of the data was conducted in August 2024. The dataset is useful for cybersecurity research, e.g. statistical analysis of domain data or feature extraction for training machine learning-based classifiers, e.g. for phishing and malware website detection. Data Files The data is located in the following individual files: benign_umbrella.json - data for 368,956 benign domains from Cisco Umbrella, benign_cesnet.json - data for 461,338 benign domains from the CESNET network, phishing.json - data for 164,425 phishing domains, and malware.json - data for 100,809 malware domains. Data Structure Both files contain a JSON array of records generated using mongoexport. The following table documents the structure of a record. Please note that: some fields may be missing (they should be interpreted as nulls), extra fields may be present (they should be ignored). Field name Field type Nullable Description domain_name String No The evaluated domain name url String No The source URL for the domain name evaluated_on Date No Date of last collection attempt source String No An identifier of the source sourced_on Date No Date of ingestion of the domain name dns Object Yes Data from DNS scan rdap Object Yes Data from RDAP or WHOIS tls Object Yes Data from TLS handshake ip_data Array of Objects Yes Array of data objects capturing the IP addresses related to the domain name DNS data (dns field) A Array of Strings No Array of IPv4 addresses AAAA Array of Strings No Array of IPv6 addresses TXT Array of Strings No Array of raw TXT values CNAME Object No The CNAME target and related IPs MX Array of Objects No Array of objects with the MX target hostname, priority and related IPs NS Array of Objects No Array of objects with the NS target hostname and related IPs SOA Object No All the SOA fields, present if found at the target domain name zone_SOA Object No The SOA fields of the target’s zone (closest point of delegation), present if found and not a record in the target domain directly dnssec Object No Flags describing the DNSSEC validation result for each record type ttls Object No The TTL values for each record type remarks Object No The zone domain name and DNSSEC flags RDAP data (rdap field) copyright_notice String No RDAP/WHOIS data usage copyright notice dnssec Bool No DNSSEC presence flag entitites Object No An object with various arrays representing the found related entity types (e.g. abuse, admin, registrant). The arrays contain objects describing the individual entities. expiration_date Date Yes The current date of expiration handle String No RDAP handle last_changed_date Date Yes The date when the domain was last changed name String No The target domain name for which the data in this object are stored nameservers Array of Strings No Nameserver hostnames provided by RDAP or WHOIS registration_date Date Yes First registration date status Array of Strings No The state of the registered object (see RFC 7483, section 10.2.2) terms_of_service_url String No URL of the RDAP usage ToS url String No URL of the RDAP entity whois_server String No WHOIS server address TLS data (tls field) cipher String No TLS cipher suite description according to IANA protocol String No One of “TLS”, ”TLSv1.2”, ”TLSv1.3” certificates Array of Objects No Array of objects representing the certificate chain, the first element is the root certificate IP data (elements in the ip_data array) ip String No The IP address from_record String No The type of the DNS record the address was captured from remarks Object No Ping round-trip time, “is alive” flag and rdap/geo/asn evaluation dates rdap Object Yes RDAP data, similar to DNS RDAP, see the JSON Schema for details geo Object Yes Geolocation data from the GeoLite2 City database (e.g. latitude, longitude, city, country, etc.) asn Object Yes Autonomous system data from the GeoLite2 ASN database (ASN, organization, network) Acknowledgements We would like to thank the OpenPhish Team for grating permission to use and publish their dataset. We also thank VirusTotal for providing us access to the API for research purposes. This dataset includes GeoLite2 data created by MaxMind, avail...

The dataset contains DNS records, IP-related features, WHOIS/RDAP information, information from TLS handshakes and certificates, and GeoIP information for 368,956 benign domains from Cisco Umbrella, 461,338 benign domains from the actual CESNET network traffic, 164,425 phishing domains from PhishTank and OpenPhish services, and 100,809 malware domains from various sources like ThreatFox, The Firebog, MISP threat intelligence platform, and other sources. The ground truth for the phishing dataset was double-check with the VirusTotal (VT) service. Domain names not considered malicious by VT have been removed from phishing and malware datasets. Similarly, benign domain names that were considered risky by VT have been removed from the benign datasets. The data was collected between March 2023 and July 2024. The final assessment of the data was conducted in August 2024.

The dataset is useful for cybersecurity research, e.g. statistical analysis of domain data or feature extraction for training machine learning-based classifiers, e.g. for phishing and malware website detection.

The dataset was created using software available in the associated GitHub repository nesfit/domainradar-dib.

Data Files

• The data is located in the following individual files:

  • benign_umbrella.json - data for 368,956 benign domains from Cisco Umbrella,
  • benign_cesnet.json - data for 461,338 benign domains from the CESNET network,
  • phishing.json - data for 164,425 phishing domains, and
  • malware.json - data for 100,809 malware domains.
• The schema.json file contains a JSON Schema with detailed description of the data entries.

Data Structure

In January 2024, ChatGPT online domain chat.openai.com registered over **** percent of its traffic as originating in the United States. Users based in India generated approximately **** percent of the total visits to the chatbot platform, while users in Indonesia accounted for *** percent of the total visits to the website. Visits from Brazil represented the fourth-largest group for the platform, generating more than **** percent of the total traffic recorded in the examined period.

This repository is part of the ITC-NetMingledApp dataset, which includes network traffic data from 36 Android applications, with each capture featuring concurrent traffic from multiple applications and smartphones. This repository contains part #2 of the data related to the Iran-Tehran scenario. Each capture is stored in a compressed file containing the relevant PCAP files of the associated applications. The PCAP files are named according to a convention: {TimeStamp}_{Application Name}{Download-Upload Speed}.pcap Part #1 of Iran-Tehran scenario is in the Tehran Dataset #1 (https://doi.org/10.17632/9frgkybxhn.1) repository.