The global number of internet users in was forecast to continuously increase between 2024 and 2029 by in total 1.3 billion users (+23.66 percent). After the fifteenth consecutive increasing year, the number of users is estimated to reach 7 billion users and therefore a new peak in 2029. Notably, the number of internet users of was continuously increasing over the past years.Depicted is the estimated number of individuals in the country or region at hand, that use the internet. As the datasource clarifies, connection quality and usage frequency are distinct aspects, not taken into account here.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of internet users in countries like the Americas and Asia.

When asked about "Attitudes towards the internet", most Japanese respondents pick "I'm concerned that my data is being misused on the internet" as an answer. 35 percent did so in our online survey in 2025. Looking to gain valuable insights about users of internet providers worldwide? Check out our reports on consumers who use internet providers. These reports give readers a thorough picture of these customers, including their identities, preferences, opinions, and methods of communication.

Web traffic statistics for the several City-Parish websites, brla.gov, city.brla.gov, Red Stick Ready, GIS, Open Data etc. Information provided by Google Analytics.

When asked about "Attitudes towards the internet", most Chinese respondents pick "It is important to me to have mobile internet access in any place" as an answer. 48 percent did so in our online survey in 2025. Looking to gain valuable insights about users of internet providers worldwide? Check out our reports on consumers who use internet providers. These reports give readers a thorough picture of these customers, including their identities, preferences, opinions, and methods of communication.

Digital technology and Internet use, website traffic strategies, by North American Industry Classification System (NAICS) and size of enterprise for Canada from 2012 to 2013.

A. SUMMARY This dataset consists of San Francisco International Airport (SFO) The aircraft landing dataset contains data about aircraft landings at SFO with monthly landing counts and landed weight by airline, region and aircraft model and type. B. HOW THE DATASET IS CREATED Data is self-reported by airlines and is only available at a monthly level. C. UPDATE PROCESS Data is available starting in July 1999 and will be updated monthly. D. HOW TO USE THIS DATASET Airport data is seasonal in nature; therefore, any comparative analyses should be done on a period-over-period basis (i.e. January 2010 vs. January 2009) as opposed to period-to-period (i.e. January 2010 vs. February 2010). It is also important to note that fact and attribute field relationships are not always 1-to-1. For example, Cargo Statistics belonging to United Airlines will appear in multiple attribute fields and are additive, which provides flexibility for the user to derive categorical Cargo Statistics as desired. E. RELATED DATASETS A summary of monthly comparative air-traffic statistics is also available on SFO’s internet site at https://www.flysfo.com/about/media/facts-statistics/air-traffic-statistics

Context There's a story behind every dataset and here's your opportunity to share yours.

Content What's inside is more than just rows and columns. Make it easy for others to get started by describing how you acquired the data and what time period it represents, too.

Acknowledgements We wouldn't be here without the help of others. If you owe any attributions or thanks, include them here along with any citations of past research.

Inspiration Your data will be in front of the world's largest data science community. What questions do you want to see answered?

The dataset contains all data used during the evaluation of statistical characteristics preservation. Archives are protected by password "trace-share" to avoid false detection by antivirus software.

For more information, see the project repository at https://github.com/Trace-Share.

Selected Attack Traces

We selected 72 different traces of network attacks obtained from various internet databases. File names refer to common names of contained vulnerabilities, malware, or attack tools.

Background Traffic Data

Publicly available dataset CSE-CIC-IDS-2018 was used as a background traffic data. The evaluation uses data from the day Thursday-01-03-2018 containing a sufficient proportion of regular traffic without any statistically significant attacks. Only traffic aimed at victim machines (range 172.31.69.0/24) is used to reduce less significant traffic.

Evaluation Results and Dataset Structure

• Traces variants (traces-normalized.zip, traces-adjusted.zip)
  • ./traces-normalized/ — normalized PCAP files and details in YAML format;
  • ./traces-adjusted/ — configuration files for traces combination in YAML format.
• Computed statistics (statistics.zip)
  • ./statistics-background/ — background traffic statistics computed by ID2T;
  • ./statistics-combination/ — combined traces statistics computed by ID2T for all adjust options (selected only combinations where ID2T provided all statistics files);
  • ./statistics-difference/ — computed mean and median differences of background and combined traffic traces.
• Evaluation results
  • statistics-difference.ipynb — file containing visualization of statistics differences.

A. SUMMARY This dataset consists of San Francisco International Airport (SFO) air traffic cargo dataset contains data about cargo volume into and out of SFO, in both metric tons and pounds, with monthly totals by airline, region and aircraft type. B. HOW THE DATASET IS CREATED Data is self-reported by airlines and is only available at a monthly level. C. UPDATE PROCESS Data is available starting in July 1999 and will be updated monthly. D. HOW TO USE THIS DATASET Airport data is seasonal in nature; therefore, any comparative analyses should be done on a period-over-period basis (i.e. January 2010 vs. January 2009) as opposed to period-to-period (i.e. January 2010 vs. February 2010). It is also important to note that fact and attribute field relationships are not always 1-to-1. For example, Cargo Statistics belonging to United Airlines will appear in multiple attribute fields and are additive, which provides flexibility for the user to derive categorical Cargo Statistics as desired. E. RELATED DATASETS A summary of monthly comparative air-traffic statistics is also available on SFO’s internet site at https://www.flysfo.com/about/media/facts-statistics/air-traffic-statistics

General data recollected for the studio " Analysis of the Quantitative Impact of Social Networks on Web Traffic of Cybermedia in the 27 Countries of the European Union". Four research questions are posed: what percentage of the total web traffic generated by cybermedia in the European Union comes from social networks? Is said percentage higher or lower than that provided through direct traffic and through the use of search engines via SEO positioning? Which social networks have a greater impact? And is there any degree of relationship between the specific weight of social networks in the web traffic of a cybermedia and circumstances such as the average duration of the user's visit, the number of page views or the bounce rate understood in its formal aspect of not performing any kind of interaction on the visited page beyond reading its content? To answer these questions, we have first proceeded to a selection of the cybermedia with the highest web traffic of the 27 countries that are currently part of the European Union after the United Kingdom left on December 31, 2020. In each nation we have selected five media using a combination of the global web traffic metrics provided by the tools Alexa (https://www.alexa.com/), which ceased to be operational on May 1, 2022, and SimilarWeb (https:// www.similarweb.com/). We have not used local metrics by country since the results obtained with these first two tools were sufficiently significant and our objective is not to establish a ranking of cybermedia by nation but to examine the relevance of social networks in their web traffic. In all cases, cybermedia whose property corresponds to a journalistic company have been selected, ruling out those belonging to telecommunications portals or service providers; in some cases they correspond to classic information companies (both newspapers and televisions) while in others they refer to digital natives, without this circumstance affecting the nature of the research proposed.
Below we have proceeded to examine the web traffic data of said cybermedia. The period corresponding to the months of October, November and December 2021 and January, February and March 2022 has been selected. We believe that this six-month stretch allows possible one-time variations to be overcome for a month, reinforcing the precision of the data obtained. To secure this data, we have used the SimilarWeb tool, currently the most precise tool that exists when examining the web traffic of a portal, although it is limited to that coming from desktops and laptops, without taking into account those that come from mobile devices, currently impossible to determine with existing measurement tools on the market. It includes:

Web traffic general data: average visit duration, pages per visit and bounce rate Web traffic origin by country Percentage of traffic generated from social media over total web traffic Distribution of web traffic generated from social networks Comparison of web traffic generated from social netwoks with direct and search procedures

When asked about "Attitudes towards the internet", most Mexican respondents pick "It is important to me to have mobile internet access in any place" as an answer. 56 percent did so in our online survey in 2025. Looking to gain valuable insights about users of internet providers worldwide? Check out our reports on consumers who use internet providers. These reports give readers a thorough picture of these customers, including their identities, preferences, opinions, and methods of communication.

The population share with mobile internet access in North America was forecast to increase between 2024 and 2029 by in total 2.9 percentage points. This overall increase does not happen continuously, notably not in 2028 and 2029. The mobile internet penetration is estimated to amount to 84.21 percent in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the population share with mobile internet access in countries like Caribbean and Europe.

Through its Employment and Financial Services (EFS) division, Seniors, Community and Social Services’ (SCSS) programs form a strong foundation of support to help many Albertans find and keep jobs. The ministry provides financial support, employment services, career resources, referrals, information on job fairs and workshops, and local labor market information. The goal is to help individuals and families gain independence by providing opportunities to enhance their skills to get jobs. The alis.alberta.ca website provides employment resources to help Albertans enhance their employability, plan for education and training, make informed career choices, and connect to and be successful in the labour market. This dataset provides information on web traffic statistics for the alis website, including information on pageviews and web sessions, demographic information for web sessions, and traffic information for the alis YouTube channel (https://www.youtube.com/user/ALISwebsite).

This dataset supports the working paper, "Repository optimisation & techniques to improve discoverability and web impact : an evaluation", currently under review for publication and available as a preprint at: https://doi.org/10.17868/65389/.

Macgregor, G. (2018). Repository optimisation techniques to improve discoverability and web impact: an evaluation. (pp. 1-13). Glasgow: University of Strathclyde [Strathprints repository]. Available: https://doi.org/10.17868/65389/

The dataset comprises a single OpenDocument Spreadsheet (.ods) format file containing seven data sheets of data pertaining to COUNTER compliant usage statistics, search query traffic from Google Search Console, web traffic data for Google Analytics and Google Scholar, and usage statistics from IRStats2. All data relate to the EPrints repository, Strathprints, based at the University of Strathclyde.

Through its Employment and Financial Services (EFS) division, Seniors, Community and Social Services’ (SCSS) programs form a strong foundation of support to help many Albertans find and keep jobs. The ministry provides financial support, employment services, career resources, referrals, information on job fairs and workshops, and local labor market information. The goal is to help individuals and families gain independence by providing opportunities to enhance their skills to get jobs. The alis.alberta.ca website provides employment resources to help Albertans enhance their employability, plan for education and training, make informed career choices, and connect to and be successful in the labour market. This dataset provides information on web traffic statistics for the alis website, including information on pageviews and web sessions, demographic information for web sessions, and traffic information for the alis YouTube channel (https://www.youtube.com/user/ALISwebsite).

Data collected between 2005 to 2007, 3% of sample collected in 2005, 51% in 2006 and 46% in 2007. This dataset contains a compilation of data collected from different sources. VOG 06/ VOG 08 (Value of Goods) : The data is derived from the information collected in the 2006 Ontario Commercial Vehicle Survey. This survey is a roadside intercept survey of truck drivers, which collects information about the trip, commodity and the vehicle. The survey primarily captures intercity trucking activity and under-represents truck flows in urban areas. The value of goods provided in this table is derived from the Commercial Vehicle Survey, but factored up to represent the overall trucking activity on the network segment for 2006 and 2008. **AADTT 2006 and ****AADTT** 2008: The data is derived from the Ministry of Transportation's (MTO) inventory of annual traffic data for the Provincial Highways. The commercial volumes are first calculated using the AADT and the Commercial Percentage values for each traffic segment. These values are then adjusted to remove variations between segments caused by fluctuations in AADT. The volume given for each direction is one-half of the total value. MTO does not maintain volume by direction. For freeway segments with core/collector configuration, the total volume is divided into four equal portions and assigned to each stream. **Hourly Truck Volumes ( WD00-23 and WN00-23): ** These fields contain estimates of average hourly volumes for a typical weekday and weekend day. The estimates are based on observed hourly distribution at more than 100 directional Commercial Vehicle Survey sites across the province, AADTT and other information. RD _NAME: Name of the road VOG 06: 2006 average daily value of goods assigned to road network link by directions. VOG 08: 2008 average daily value of goods assigned to road network link by directions. AADTT 2006: 2006 Annual Average Daily Truck Traffic; it is the truck volume assigned to road network link by directions. AADTT 2008: 2008 Annual Average Daily Truck Traffic; it is the truck volume assigned to road network link by directions. WD 00-23: 2008 Weekday ( WD ) hourly truck volume; 00 - 23 represents starting hour of the day (e.g. 12 represents 12 P.M. - 1 P.M.). WN 00-23: 2008 Weekend ( WN ) hourly truck volume; 00 - 23 represents starting hour of the day (e.g. 12 represents 12 P.M. - 1 P.M.). *[ WD]: Week day *[VOG]: Value of Goods *[AADTT]: Annual Average Daily Truck Traffic *[WN]: Week end *[RD]: Road *[WD]: Week day *[MTO]: Ministry of Transportation *[AADT]: Annual Average Daily Traffic

TrojAI cyber-network-c2-mar2024 Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of ResNet18 and ResNet34 neural network models that classify botnet command and control (c2) and benign network traffic packets trained on the USTC-TFC2016 dataset. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.

The annual average daily traffic (AMT) of a road section is obtained by calculating the year-over-year average of the number of vehicles circulating on that section, in all senses, over a day. These delayed traffic data are usually accompanied by an estimate (in percentage) of the number of heavy goods vehicles in their composition.

Using a user DNS fingerprint allows one to identify a specific network user regardless of the knowledge of his IP address. This method is proper, for example, when examining the behavior of a monitored network user in more depth. In contrast to other studies, this work introduces a dataset for possible user identification based only on the knowledge of its DNS fingerprint created from the previously sent DNS queries.

We created a large dataset from the real network traffic of a metropolitan Internet service provider. The dataset was created from 2.3 billion DNS queries representing 6.2 million different domain names. The data collection took place over three months from 12/2023 to 02/2024.

The dataset contains a detailed user activity description in the sense of overall daily activity statistics and detailed 24-hour activity statistics. Each dataset record contains a list of 1137 classification attributes. The absolutely unique feature of this data set is the classification of user activity based on categories of content accessed by a user.

The new dataset can be used for the creation of machine learning models, allowing the identification of a specific user without direct knowledge of their IP addresses or additional network location information. The dataset can also serve as a reference dataset for the creation of DNS fingerprints of users.