In the second quarter of 2025, mobile devices (excluding tablets) accounted for 62.54 percent of global website traffic. Since consistently maintaining a share of around 50 percent beginning in 2017, mobile usage surpassed this threshold in 2020 and has demonstrated steady growth in its dominance of global web access. Mobile traffic Due to low infrastructure and financial restraints, many emerging digital markets skipped the desktop internet phase entirely and moved straight onto mobile internet via smartphone and tablet devices. India is a prime example of a market with a significant mobile-first online population. Other countries with a significant share of mobile internet traffic include Nigeria, Ghana and Kenya. In most African markets, mobile accounts for more than half of the web traffic. By contrast, mobile only makes up around 45.49 percent of online traffic in the United States. Mobile usage The most popular mobile internet activities worldwide include watching movies or videos online, e-mail usage and accessing social media. Apps are a very popular way to watch video on the go and the most-downloaded entertainment apps in the Apple App Store are Netflix, Tencent Video and Amazon Prime Video.

Streaming is by far the predominant type of traffic in communication networks. With thispublic dataset, we provide 1,081 hours of time-synchronous video measurements at network, transport, and application layer with the native YouTube streaming client on mobile devices. The dataset includes 80 network scenarios with 171 different individual bandwidth settings measured in 5,181 runs with limited bandwidth, 1,939 runs with emulated 3G/4G traces, and 4,022 runs with pre-defined bandwidth changes. This corresponds to 332GB video payload. We present the most relevant quality indicators for scientific use, i.e., initial playback delay, streaming video quality, adaptive video quality changes, video rebuffering events, and streaming phases.

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 6.1 billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.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 smartphone users in countries like Australia & Oceania and Asia.

Context

The data presented here was obtained in a Kali Machine from University of Cincinnati,Cincinnati,OHIO by carrying out packet captures for 1 hour during the evening on Oct 9th,2023 using Wireshark.This dataset consists of 394137 instances were obtained and stored in a CSV (Comma Separated Values) file.This large dataset could be used utilised for different machine learning applications for instance classification of Network traffic,Network performance monitoring,Network Security Management , Network Traffic Management ,network intrusion detection and anomaly detection.

The dataset can be used for a variety of machine learning tasks, such as network intrusion detection, traffic classification, and anomaly detection.

Content :

This network traffic dataset consists of 7 features.Each instance contains the information of source and destination IP addresses, The majority of the properties are numeric in nature, however there are also nominal and date kinds due to the Timestamp.

The network traffic flow statistics (No. Time Source Destination Protocol Length Info) were obtained using Wireshark (https://www.wireshark.org/).

Dataset Columns:

No : Number of Instance. Timestamp : Timestamp of instance of network traffic Source IP: IP address of Source Destination IP: IP address of Destination Portocol: Protocol used by the instance Length: Length of Instance Info: Information of Traffic Instance

Acknowledgements :

I would like thank University of Cincinnati for giving the infrastructure for generation of network traffic data set.

Ravikumar Gattu , Susmitha Choppadandi

Inspiration : This dataset goes beyond the majority of network traffic classification datasets, which only identify the type of application (WWW, DNS, ICMP,ARP,RARP) that an IP flow contains. Instead, it generates machine learning models that can identify specific applications (like Tiktok,Wikipedia,Instagram,Youtube,Websites,Blogs etc.) from IP flow statistics (there are currently 25 applications in total).

**Dataset License: ** CC0: Public Domain

Dataset Usages : This dataset can be used for different machine learning applications in the field of cybersecurity such as classification of Network traffic,Network performance monitoring,Network Security Management , Network Traffic Management ,network intrusion detection and anomaly detection.

ML techniques benefits from this Dataset :

This dataset is highly useful because it consists of 394137 instances of network traffic data obtained by using the 25 applications on a public,private and Enterprise networks.Also,the dataset consists of very important features that can be used for most of the applications of Machine learning in cybersecurity.Here are few of the potential machine learning applications that could be benefited from this dataset are :

1. Network Performance Monitoring : This large network traffic data set can be utilised for analysing the network traffic to identifying the network patterns in the network .This help in designing the network security algorithms for minimise the network probelms.

2. Anamoly Detection : Large network traffic dataset can be utilised training the machine learning models for finding the irregularitues in the traffic which could help identify the cyber attacks.

3.Network Intrusion Detection : This large dataset could be utilised for machine algorithms training and designing the models for detection of the traffic issues,Malicious traffic network attacks and DOS attacks as well.

This dataset encompasses mobile web clickstream behavior on any browser, collected from over 150,000 triple-opt-in first-party US Daily Active Users (DAU). Use it for measurement, attribution or path to purchase and consumer journey understanding. Full URL deliverable available including searches.

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.

Context This dataset is a consolidated and cleaned CSV version of the ISCX VPN-nonVPN 2016 dataset from the Canadian Institute for Cybersecurity (CIC) at the University of New Brunswick. The original dataset was created to characterize and identify different types of network traffic, which is crucial for network management, Quality of Service (QoS) optimization, and cybersecurity.

This single CSV file combines the multiple .arff files from the original dataset, making it easier to use for data analysis and machine learning projects in Python.

Content The dataset contains network flow features extracted from packet captures (PCAPs). Each row represents a single network flow and has been labeled with the specific application type and whether it was routed through a VPN.

Features (X): Include over 20 time-related flow features like duration, flowBytesPerSecond, flowPktsPerSecond, min_active, max_idle, etc. These features describe the timing, duration, and volume of the data flows.

Target (y): The target column, traffic_type, is a multi-class label describing the application and connection type (e.g., VPN-CHAT, NonVPN-STREAMING, VPN-Browse).

Potential Uses & Inspiration 🚀 Multi-Class Classification: Can you build a model to accurately identify the specific application generating the traffic?

Binary Classification: Can you distinguish between VPN and Non-VPN traffic, regardless of the application?

Resource Allocation: Predict which types of traffic (e.g., Streaming) require more bandwidth, helping to build smarter network management tools.

Federated Learning: This dataset is ideal for simulating a Federated Learning environment where data from different "users" (applications) is used to train a central model without sharing raw data.

Verified dataset of 2025 device usage: share of global web traffic, mobile commerce share of transactions, US daily time spent, app vs web breakdown, and tablet decline.

This dataset provides detailed insights and best practices for tracking and measuring local SEO performance across a range of critical metrics, including Google Business Profile engagement, local keyword rankings, website traffic from local searches, citation management, mobile optimization, and ROI calculation. The data is based on expert analysis and recommendations to help local businesses optimize their local search visibility and drive measurable results.

Switzerland is leading the ranking by population share with mobile internet access, recording 95.06 percent. Following closely behind is Ukraine with 95.06 percent, while Moldova is trailing the ranking with 46.83 percent, resulting in a difference of 48.23 percentage points to the ranking leader, Switzerland. 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).

For the evaluation of OS fingerprinting methods, we need a dataset with the following requirements:

First, the dataset needs to be big enough to capture the variability of the data. In this case, we need many connections from different operating systems.

Second, the dataset needs to be annotated, which means that the corresponding operating system needs to be known for each network connection captured in the dataset. Therefore, we cannot just capture any network traffic for our dataset; we need to be able to determine the OS reliably.

To overcome these issues, we have decided to create the dataset from the traffic of several web servers at our university. This allows us to address the first issue by collecting traces from thousands of devices ranging from user computers and mobile phones to web crawlers and other servers. The ground truth values are obtained from the HTTP User-Agent, which resolves the second of the presented issues. Even though most traffic is encrypted, the User-Agent can be recovered from the web server logs that record every connection’s details. By correlating the IP address and timestamp of each log record to the captured traffic, we can add the ground truth to the dataset.

For this dataset, we have selected a cluster of five web servers that host 475 unique university domains for public websites. The monitoring point recording the traffic was placed at the backbone network connecting the university to the Internet.

The dataset used in this paper was collected from approximately 8 hours of university web traffic throughout a single workday. The logs were collected from Microsoft IIS web servers and converted from W3C extended logging format to JSON. The logs are referred to as web logs and are used to annotate the records generated from packet capture obtained by using a network probe tapped into the link to the Internet.

The entire dataset creation process consists of seven steps:

The packet capture was processed by the Flowmon flow exporter (https://www.flowmon.com) to obtain primary flow data containing information from TLS and HTTP protocols.

Additional statistical features were extracted using GoFlows flow exporter (https://github.com/CN-TU/go-flows).

The primary flows were filtered to remove incomplete records and network scans.

The flows from both exporters were merged together into records containing fields from both sources.

Web logs were filtered to cover the same time frame as the flow records.

Web logs were paired with the flow records based on shared properties (IP address, port, time).

The last step was to convert the User-Agent values into the operating system using a Python version of the open-source tool ua-parser (https://github.com/ua-parser/uap-python). We replaced the unstructured User-Agent string in the records with the resulting OS.

The collected and enriched flows contain 111 data fields that can be used as features for OS fingerprinting or any other data analyses. The fields grouped by their area are listed below:

basic flow properties - flow_ID;start;end;L3 PROTO;L4 PROTO;BYTES A;PACKETS A;SRC IP;DST IP;TCP flags A;SRC port;DST port;packetTotalCountforward;packetTotalCountbackward;flowDirection;flowEndReason;

IP parameters - IP ToS;maximumTTLforward;maximumTTLbackward;IPv4DontFragmentforward;IPv4DontFragmentbackward;

TCP parameters - TCP SYN Size;TCP Win Size;TCP SYN TTL;tcpTimestampFirstPacketbackward;tcpOptionWindowScaleforward;tcpOptionWindowScalebackward;tcpOptionSelectiveAckPermittedforward;tcpOptionSelectiveAckPermittedbackward;tcpOptionMaximumSegmentSizeforward;tcpOptionMaximumSegmentSizebackward;tcpOptionNoOperationforward;tcpOptionNoOperationbackward;synAckFlag;tcpTimestampFirstPacketforward;

HTTP - HTTP Request Host;URL;

User-agent - UA OS family;UA OS major;UA OS minor;UA OS patch;UA OS patch minor;

TLS - TLS_CONTENT_TYPE;TLS_HANDSHAKE_TYPE;TLS_SETUP_TIME;TLS_SERVER_VERSION;TLS_SERVER_RANDOM;TLS_SERVER_SESSION_ID;TLS_CIPHER_SUITE;TLS_ALPN;TLS_SNI;TLS_SNI_LENGTH;TLS_CLIENT_VERSION;TLS_CIPHER_SUITES;TLS_CLIENT_RANDOM;TLS_CLIENT_SESSION_ID;TLS_EXTENSION_TYPES;TLS_EXTENSION_LENGTHS;TLS_ELLIPTIC_CURVES;TLS_EC_POINT_FORMATS;TLS_CLIENT_KEY_LENGTH;TLS_ISSUER_CN;TLS_SUBJECT_CN;TLS_SUBJECT_ON;TLS_VALIDITY_NOT_BEFORE;TLS_VALIDITY_NOT_AFTER;TLS_SIGNATURE_ALG;TLS_PUBLIC_KEY_ALG;TLS_PUBLIC_KEY_LENGTH;TLS_JA3_FINGERPRINT;

Packet timings - NPM_CLIENT_NETWORK_TIME;NPM_SERVER_NETWORK_TIME;NPM_SERVER_RESPONSE_TIME;NPM_ROUND_TRIP_TIME;NPM_RESPONSE_TIMEOUTS_A;NPM_RESPONSE_TIMEOUTS_B;NPM_TCP_RETRANSMISSION_A;NPM_TCP_RETRANSMISSION_B;NPM_TCP_OUT_OF_ORDER_A;NPM_TCP_OUT_OF_ORDER_B;NPM_JITTER_DEV_A;NPM_JITTER_AVG_A;NPM_JITTER_MIN_A;NPM_JITTER_MAX_A;NPM_DELAY_DEV_A;NPM_DELAY_AVG_A;NPM_DELAY_MIN_A;NPM_DELAY_MAX_A;NPM_DELAY_HISTOGRAM_1_A;NPM_DELAY_HISTOGRAM_2_A;NPM_DELAY_HISTOGRAM_3_A;NPM_DELAY_HISTOGRAM_4_A;NPM_DELAY_HISTOGRAM_5_A;NPM_DELAY_HISTOGRAM_6_A;NPM_DELAY_HISTOGRAM_7_A;NPM_JITTER_DEV_B;NPM_JITTER_AVG_B;NPM_JITTER_MIN_B;NPM_JITTER_MAX_B;NPM_DELAY_DEV_B;NPM_DELAY_AVG_B;NPM_DELAY_MIN_B;NPM_DELAY_MAX_B;NPM_DELAY_HISTOGRAM_1_B;NPM_DELAY_HISTOGRAM_2_B;NPM_DELAY_HISTOGRAM_3_B;NPM_DELAY_HISTOGRAM_4_B;NPM_DELAY_HISTOGRAM_5_B;NPM_DELAY_HISTOGRAM_6_B;NPM_DELAY_HISTOGRAM_7_B;

ICMP - ICMP TYPE;

The details of OS distribution grouped by the OS family are summarized in the table below. The Other OS family contains records generated by web crawling bots that do not include OS information in the User-Agent.

    OS Family
    Number of flows




    Other
    42474


    Windows
    40349


    Android
    10290


    iOS
    8840


    Mac OS X
    5324


    Linux
    1589


    Ubuntu
    653


    Fedora
    88


    Chrome OS
    53


    Symbian OS
    1


    Slackware
    1


    Linux Mint
    1

This dataset encompasses mobile app usage, web clickstream and location visitation behavior, collected from over 150,000 triple-opt-in first-party US Daily Active Users (DAU). The only omnichannel meter at scale representing iOS and Android platforms.

The dataset, provided by Shanghai Telecom, contains more than 7.2 million records of accessing the Interent through 3,233 base stations from 9,481 mobile phones for six months. For example, the following figure shows the distribution of base stations. Each node denotes a base station in Shanghai, China. This dataset could help researchers to evaluate their solution in mobile edge computing topic such as edge server placement, service migration, service recommendation, etc.

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As shown in the following table, the Telecom dataset shows 6 parameters such as Month, Data, Start Time, End Time, Base Station Location, Mobile Phone ID. The trajectory of users can be found by the dataset.

Citation

The telecom dataset is available free of charge for educational and non-commercial purposes. The Telecom data should be used in any scientific or educational study/research. Redistribution of this data to any other third party is not permited.In exchange, we kindly request that you make available to us the results of running the telecom dataset. You must cite the following papers when using this Telecom dataset.

[1] Yuanzhe Li, Ao Zhou, Xiao Ma, Shangguang Wang, Profit-aware Edge Server Placement, IEEE Internet of Things Journal, 2022, vol.9, no.1 ,pp.55-67 PDF Sourcecode

[2] Y. Guo, S. Wang, A. Zhou, J. Xu, J. Yuan, C. Hsu. User Allocation‐aware Edge Cloud Placement in Mobile Edge Computing, Software: Practice and Experience, vol. 50, no. 5, pp. 489-502, 2020.PDF Sourcecode

[3] S. Wang, Y. Guo, N. Zhang, P. Yang, A. Zhou, X. Shen. Delay-aware Microservice Coordination in Mobile Edge Computing: A Reinforcement Learning Approach, IEEE Transactions on Mobile Computing, vol. 20, no.3, pp.939-953, 2021. PDF

Fixed and mobile broadband internet traffic statistics come from International Telecommunication Union (ITU), the United Nations specialized agency for information and communication technologies (ICTs).

ITU collects Internet traffic statistics on fixed and mobile broadband (inside the country) through its annual World Telecommunication/ICT Indicators short and long questionnaires according to the methodology provided in the Handbook for the Collection of Administrative Data on Telecommunications/ICT. Mobile network operators (MNOs) and Internet service providers (IPSs) systematically measure Internet data usage (both upload and download) of their customers, which is the basis of traffic statistics. Data from MNOs and ISPs are collected and aggregated by national telecommunications/ICT regulatory authorities or ministries and reported to ITU in the World Telecommunication/ICT indicators questionnaire series. Data that are unavailable from the questionnaires are compiled from publicly available sources from regulators and ministries, and from the OECD Broadband statistics.

The statistics on the internet traffic include:

Fixed-broadband internet traffic refers to the annual total volume of data traffic generated by fixed-broadband subscribers measured at the end-user access point. It should be measured by adding up download and upload traffic. Internet traffic refers to open Internet traffic generated or consumed by users connected to the Internet. Wholesale traffic (provided for another operator), walled-garden traffic, and IPTV and cable-TV traffic should be excluded. Traffic data should be collected from fixed operators offering Internet connections or ISPs by national regulatory authorities and ministries.

Mobile broadband Internet traffic (within the country) refers to the annual total broadband traffic volumes (uploaded and downloaded) originated within the country from 3G or other more advanced mobile networks, including evolutions, or equivalent standards in terms of data transmission speeds. Wholesale and walled-garden traffic should be excluded. Traffic should be measured at the end-user access point.

Mobile broadband Internet traffic (outside the country) refers to the annual total broadband traffic volumes originated outside the country from 3G or other more advanced mobile networks, including evolutions or equivalent standards in terms of data transmission speeds. Wholesale and walled-garden traffic should be excluded. Traffic should be collected and aggregated at the country level for all customers of domestic operators roaming outside the country. Traffic should be measured at the end-user access point.

The number of mobile broadband connections per 100 inhabitants in the United States was forecast to continuously increase between 2024 and 2029 by in total 21.1 connections (+11.49 percent). After the fifteenth consecutive increasing year, the mobile broadband penetration is estimated to reach 204.76 connections and therefore a new peak in 2029. Notably, the number of mobile broadband connections per 100 inhabitants of was continuously increasing over the past years.Mobile broadband connections include cellular connections with a download speed of at least 256 kbit/s (without satellite or fixed-wireless connections). Cellular Internet-of-Things (IoT) or machine-to-machine (M2M) connections are excluded. 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 mobile broadband connections per 100 inhabitants in countries like Canada and Mexico.

In 2022, over half of the web traffic was accessed through mobile devices. By reducing the energy consumption of mobile web apps, we can not only extend the battery life of our devices, but also make a significant contribution to energy conservation efforts. For example, if we could save only 5% of the energy used by web apps, we estimate that it would be enough to shut down one of the nuclear reactors in Fukushima. This paper presents a comprehensive overview of energy-saving experiments and related approaches for mobile web apps, relevant for researchers and practitioners. To achieve this objective, we conducted a systematic literature review and identified 44 primary studies for inclusion. Through the mapping and analysis of scientific papers, this work contributes: (1) an overview of the energy-draining aspects of mobile web apps, (2) a comprehensive description of the methodology used for the energy-saving experiments, and (3) a categorization and synthesis of various energy-saving approaches.

This dataset is available on Brisbane City Council’s open data website – data.brisbane.qld.gov.au. The site provides additional features for viewing and interacting with the data and for downloading the data in various formats.

The Brisbane City Council parking occupancy forecasting data is provided to be accessed by third party web or app developers to develop tools to provide Brisbane residents and visitors with likely parking availability within a paid parking area.

The parking occupancy forecasting data is compiled using advanced analytics and machine learning to estimate paid parking availability. The solution uses parking occupancy survey data, parking meter transaction data and other traffic and environmental data.

This dataset is linked to the open data called Parking — Meter locations. The field called MOBILE_ZONE is used to link the datasets. MOBILE_ZONE is a seven-digit mobile payment zone number that may include one or many parking meter numbers.

Additional information on parking meters can be found on the Brisbane City Council website.

The Brisbane City Council parking occupancy forecasting data includes parking data for all of Council’s parking meters. The data attributes used in this resource and their descriptions can be found in the Parking — Occupancy forecasting — metadata — CSV resource in this dataset.

The Data and resources section of this dataset contains further information for this dataset.

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 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.

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. 50 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.