As of the last quarter of 2023, 31.57 percent of web traffic in the United States originated from mobile devices, down from 49.51 percent in the fourth quarter of 2022. In comparison, over half of web traffic worldwide was generated via mobile in the last examined period.

As of the first quarter of 2019, Yandex.Market was an absolute dominant in the online price comparison market across Russia with over *** monthly visitors. Tiu.ru followed the leader with roughly ** million visitors per month. The website listed third, Regmarkets, accounted for nearly ** million monthly visitors over the observed timeframe.

Across popular online marketplace websites visited by users in Australia in February 2025, temu.com registered the highest growth in its website traffic compared to the previous year, with an annual growth of over 56 percent. In comparison, ebay.com.au saw a decrease in its website traffic compared to the previous year, with an annual decrease of around 11.9 percent.

Difference uses Google Analytics as the Baseline. Results based on Paired t-Test for Hypotheses Supported.

Network traffic datasets created by Single Flow Time Series Analysis

Datasets were created for the paper: Network Traffic Classification based on Single Flow Time Series Analysis -- Josef Koumar, Karel Hynek, Tomáš Čejka -- which was published at The 19th International Conference on Network and Service Management (CNSM) 2023. Please cite usage of our datasets as:

J. Koumar, K. Hynek and T. Čejka, "Network Traffic Classification Based on Single Flow Time Series Analysis," 2023 19th International Conference on Network and Service Management (CNSM), Niagara Falls, ON, Canada, 2023, pp. 1-7, doi: 10.23919/CNSM59352.2023.10327876.

This Zenodo repository contains 23 datasets created from 15 well-known published datasets which are cited in the table below. Each dataset contains 69 features created by Time Series Analysis of Single Flow Time Series. The detailed description of features from datasets is in the file: feature_description.pdf

In the following table is a description of each dataset file:

File name Detection problem Citation of original raw dataset

botnet_binary.csv Binary detection of botnet S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014.

botnet_multiclass.csv Multi-class classification of botnet S. García et al. An Empirical Comparison of Botnet Detection Methods. Computers & Security, 45:100–123, 2014.

cryptomining_design.csv Binary detection of cryptomining; the design part Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022

cryptomining_evaluation.csv Binary detection of cryptomining; the evaluation part Richard Plný et al. Datasets of Cryptomining Communication. Zenodo, October 2022

dns_malware.csv Binary detection of malware DNS Samaneh Mahdavifar et al. Classifying Malicious Domains using DNS Traffic Analysis. In DASC/PiCom/CBDCom/CyberSciTech 2021, pages 60–67. IEEE, 2021.

doh_cic.csv Binary detection of DoH

Mohammadreza MontazeriShatoori et al. Detection of doh tunnels using time-series classification of encrypted traffic. In DASC/PiCom/CBDCom/CyberSciTech 2020, pages 63–70. IEEE, 2020

doh_real_world.csv Binary detection of DoH Kamil Jeřábek et al. Collection of datasets with DNS over HTTPS traffic. Data in Brief, 42:108310, 2022

dos.csv Binary detection of DoS Nickolaos Koroniotis et al. Towards the development of realistic botnet dataset in the Internet of Things for network forensic analytics: Bot-IoT dataset. Future Gener. Comput. Syst., 100:779–796, 2019.

edge_iiot_binary.csv Binary detection of IoT malware Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022.

edge_iiot_multiclass.csv Multi-class classification of IoT malware Mohamed Amine Ferrag et al. Edge-iiotset: A new comprehensive realistic cyber security dataset of iot and iiot applications: Centralized and federated learning, 2022.

https_brute_force.csv Binary detection of HTTPS Brute Force Jan Luxemburk et al. HTTPS Brute-force dataset with extended network flows, November 2020

ids_cic_binary.csv Binary detection of intrusion in IDS Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018.

ids_cic_multiclass.csv Multi-class classification of intrusion in IDS Iman Sharafaldin et al. Toward generating a new intrusion detection dataset and intrusion traffic characterization. ICISSp, 1:108–116, 2018.

ids_unsw_nb_15_binary.csv Binary detection of intrusion in IDS Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015.

ids_unsw_nb_15_multiclass.csv Multi-class classification of intrusion in IDS Nour Moustafa and Jill Slay. Unsw-nb15: a comprehensive data set for network intrusion detection systems (unsw-nb15 network data set). In 2015 military communications and information systems conference (MilCIS), pages 1–6. IEEE, 2015.

iot_23.csv Binary detection of IoT malware Sebastian Garcia et al. IoT-23: A labeled dataset with malicious and benign IoT network traffic, January 2020. More details here https://www.stratosphereips.org /datasets-iot23

ton_iot_binary.csv Binary detection of IoT malware Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021

ton_iot_multiclass.csv Multi-class classification of IoT malware Nour Moustafa. A new distributed architecture for evaluating ai-based security systems at the edge: Network ton iot datasets. Sustainable Cities and Society, 72:102994, 2021

tor_binary.csv Binary detection of TOR Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017.

tor_multiclass.csv Multi-class classification of TOR Arash Habibi Lashkari et al. Characterization of Tor Traffic using Time based Features. In ICISSP 2017, pages 253–262. SciTePress, 2017.

vpn_iscx_binary.csv Binary detection of VPN Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016.

vpn_iscx_multiclass.csv Multi-class classification of VPN Gerard Draper-Gil et al. Characterization of Encrypted and VPN Traffic Using Time-related. In ICISSP, pages 407–414, 2016.

vpn_vnat_binary.csv Binary detection of VPN Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022

vpn_vnat_multiclass.csv Multi-class classification of VPN Steven Jorgensen et al. Extensible Machine Learning for Encrypted Network Traffic Application Labeling via Uncertainty Quantification. CoRR, abs/2205.05628, 2022

Comparison of user, site, and network-centric approaches to web analytics data collection showing advantages, disadvantages, and examples of each approach at the time of the study.

Comparison of definitions of total visits, unique visitors, bounce rate, and session duration conceptually and for the two analytics platforms: Google Analytics and SimilarWeb.

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.

Traffic Volume for Key Brisbane Corridors. Includes traffic volumes, travel times and incidents.

This dataset will no longer be updated. Data is being published in a new format in a new dataset called Traffic Management — Key Corridor — Monthly Performance Report.

Information on Traffic Management is available on the Brisbane City Council website.

This dataset contains the following resources:1. Traffic Volume for Key Brisbane Corridors.

Excel file containing: * 6\-Month Average Daily, AM \& PM Peak Traffic Volume * Network Daily Traffic Volume Comparison * 6\-Month Average AM \& PM Peak Travel Time * Network Travel Time Comparison * Incident Data * Note: volume day of the week and TT day of week was discontinued and is not included from Jul\-Dec 2015

1. Traffic Volume for Key Brisbane Corridors.

Excel file containing: * 6\-Month Average Daily, AM \& PM Peak Traffic Volume * Network Daily Traffic Volume Comparison * 6\-Month Average AM \& PM Peak Travel Time * Network Travel Time Comparison * Incident Data * Average daily traffic volume for each day of the week (veh/day) * Travel time per kilometre by day of the week (mm:ss/km)

In 2023, mobile devices accounted for approximately 65.1 percent of total web pages served to web browsers in Montenegro. For comparison, tablet devices represented only 1.6 percent of web traffic.

https://github.com/EldhosePaul-2023/meshlab

Goal of the project is to measure and compare the performance of several forwarding techniques on different devices.

Compare the following four forwarding techniques/implementions regarding their performance:

• eBPF (TC or XDP)
• IP forwarding
• IP forwarding with software offloading
• IP forwarding with hardware offloading

In Australia in 2024, Facebook was the leading source of web traffic referrals, driving 65 percent of clicks or taps to third-party websites. In comparison, Instagram accounted for 11.6 percent, while Pinterest contributed 9.2 percent.

As of November 2021, in over 90 percent of cases, Hungarians accessed the tabloid site, blikk.hu using mobile devices. Furthermore, Hungarians mostly used their phones to read online news on websites such as index.hu or origo.hu. By comparison, mobile devices accounted for less than 24 percent of YouTube's web traffic over the same period.

Ecomarket.ru saw a *** percent growth in web traffic in July 2021 compared to the equivalent period of the previous year. Sbermarket, which was present in most regions in the country, reported a *** percent traffic growth. Another major online grocery retailer, Perekrestok, on the other hand, reported an over ** percent traffic loss during the given timeframe.

In April 2024, UK-based electronics retailer Currys PLC garnered some 15.4 million visits to its website, currys.co.uk. In comparison, online marketplace argos.co.uk had 35.6 million website visits during the same month.

As of third quarter 2019, local companies in Vietnam accounted for a larger share in website traffic of e-commerce businesses with 72 percent. In comparison, the share of local companies in the Philippines was at only four percent.

As of December 2024, Google's Chrome web browser had the largest share of web traffic in the United Arab Emirates, at around ** percent. In comparison, Safari web browser had around **** percent, a **** percent drop from the previous year.

January 2024, Netflix.com generated over 412 million visits in the United States. Traffic to the SVoD platform increased by seven percent compared to the previous month. Overall, Netflix was the leading subscription video-on-demand service in terms of traffic during the examined period. Between the second half of 2022 and the beginning of 2023, search and visit volume trends on streaming sites in the market appeared to have normalized after the usage increase brought by the COVID-19 pandemic in 2020 and 2021.

From February to July 2024, February was the month that had the most website traffic to ebay.com. The consumer-to-consumer (C2C) e-commerce website reached a total of over *** million visits in that month, with the majority being from mobile devices. Popularity on multiple fronts Although eBay is popular on mobile devices, monthly downloads of its mobile app have been trending in the wrong direction since peaking in June 2020 at **** million. Still, in April 2023, ebay.com was the second most popular e-commerce and shopping website worldwide, accounting for more than ***** percent of visits to sites in this category. Big numbers declining In the second quarter of 2023, eBay’s gross merchandise volume (GMV) amounted to nearly **** billion U.S. dollars. That is no small number, but is only a small increase compared to the lowest GMV recorded by the company since the first quarter of 2020 - **** billion U.S. dollars in the third quarter of 2022 - and that’s not the only figure on the decline for eBay. The e-commerce platform had approximately *** million active buyers in the second quarter of 2022, and a year later that number was down *** percent to *** million.

In December 2023, Amazon.com was the leading online shopping website in the United States. During the measured period, the sprawling platform accounted for over 45 percent of desktop traffic in the e-commerce and shopping subcategory. In second place on the list was eBay.com, with 9.22 percent of visitors. Walmart ranked third with a bit less than six percent of web traffic. Why customers browse on Amazon The main reason behind the outstanding online traffic to Amazon is user behavior throughout the customer journey. Amazon serves as a search engine for U.S. consumers, with 73 percent browsing it for inspiration and product discovery. Another 65 percent of U.S. shoppers landed on Amazon to look for products and compare products. In turn, Google is left third in the ranking of most used platforms. Generational differences In the beauty segment, the customer journey is more likely to start on Amazon among senior consumers. In the United States, 44 percent of Baby Boomers started their search of beauty products on the marketplace, while only 35 percent of Gen Z consumers reported doing the same.

Around ** percent of the web traffic in Saudi Arabia in December 2024 was through Chrome. In comparison, Safari accounted for approximately ** percent of the web traffic in the country.