In November 2025, mobile devices excluding tablets accounted for over ***** percent of web page views worldwide. Meanwhile, over ***** percent of webpage views in Africa were generated via mobile. In contrast, just over half of web traffic in North America still took place via desktop connections, with mobile only accounting for ***** percent of total web traffic. While regional infrastructure remains an important factor in broadband vs. mobile coverage, most of the world has had their eyes on the recent 5G rollout across the globe, spearheaded by tech leaders China and the United States. The number of mobile 5G subscriptions worldwide is forecast to reach more than ***** billion by 2028. Social media: room for growth in Africa and southern Asia Overall, more than ** percent of the world’s mobile internet subscribers are also active on social media. A fast-growing market, with newcomers such as TikTok taking the world by storm, marketers have been cashing in on social media’s reach. Overall, social media penetration is highest in Europe and America, while in Africa and southern Asia, there is still room for growth. As of 2021, Facebook and Google-owned YouTube are the most popular social media platforms worldwide. Facebook and Instagram are most effective With nearly ***** billion users, it is no wonder that Facebook remains the social media avenue of choice for the majority of marketers across the world. Instagram, meanwhile, was the second most popular outlet. Both platforms are low-cost and support short-form content, known for its universal consumer appeal and answering to the most important benefits of using these kinds of platforms for business and advertising purposes.

Internet traffic volume measures global IP traffic, or the amount of data being sent and received over the internet globally each month. Data and forecasts are sourced from Cisco Systems Inc.

In the first half of 2021, video accounted for over **** of global traffic. Social occupied the next largest share at **** percent, while web browsing accounted for around a *****. Audio accounted for only **** percent of traffic worldwide.

The statistic shows a forecast for the worldwide data volume of internet video to TV from 2016 to 2021, in petabytes per month. In 2018, global traffic from internet videos viewed through television devices such as internet-ready set-top boxes is estimated to reach 23,387 petabytes per month.

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.

This dataset provides a comprehensive overview of global internet usage as of 2024. It includes the number of internet users in each country, the percentage of the population with internet access, and the total internet traffic generated. This dataset can be used to analyze trends in internet adoption, digital inequality, and the potential impact of the internet on various sectors of the global economy.

Internet Traffic: Network Access Point: DIRECTV data was reported at 2,234.420 GB in 27 Sep 2020. This records an increase from the previous number of 2,007.590 GB for 26 Sep 2020. Internet Traffic: Network Access Point: DIRECTV data is updated daily, averaging 1,380.975 GB from Mar 2020 (Median) to 27 Sep 2020, with 182 observations. The data reached an all-time high of 2,368.170 GB in 25 Sep 2020 and a record low of 991.650 GB in 02 May 2020. Internet Traffic: Network Access Point: DIRECTV data remains active status in CEIC and is reported by Communications Regulation Commission. The data is categorized under Global Database’s Colombia – Table CO.TB004: Internet Traffic: by Provider.

According to data collected in the first half of 2021, five leading tech firms accounted for more than half of global data traffic. Google accounted for around ******* of global data traffic, with its share including traffic driven by its subsidiary video platform YouTube. Google is followed by Facebook with a share of around ** percent, while Netflix accounts for around * percent of traffic.

In 1995, only 0.77% of the world’s population was online. Fast forward to 2025, and nearly two-thirds of humanity now live a connected life. Whether it's streaming news on a smart fridge in Texas or running a full business from a beach in Bali, the internet is no longer a...

Our Data Center Traffic web traffic dataset adds a critical layer of protection to your marketing stack by identifying and filtering web traffic generated from the IP addresses of suspicious data center sources. These signals often come from bots, scrapers, or emulators that disguise themselves as real users but deliver no value to your campaigns. Left unchecked, they can distort performance metrics, inflate engagement numbers, and drain your ad budget.

Leverage our web traffic data solutions for the following use cases: - Invalid Web Traffic Prevention - Data Hygiene & Model Building - Audience Quality Assurance - Trial & Partnership Transparency

With AdPreference, expect the following key benefits through our partnership: - Protect Your Ad Spend - Enhance Cybersecurity - Improve Campaign Performance - Strengthen Brand Integrity - Reduce Ad Fraud

By continuously monitoring and updating our web traffic intelligence, we empower marketers, agencies, and platforms to distinguish legitimate human activity from fraudulent traffic at scale. The result is cleaner datasets, more accurate audience models, and campaigns that perform against true user engagement. With our web traffic dataset, you can protect ad spend, maintain data integrity, and reinforce trust across your digital ecosystem.

For more information, please visit https://www.adpreference.co/

Internet Traffic: Local: Edatel S.A data was reported at 0.000 GB in 27 Sep 2020. This stayed constant from the previous number of 0.000 GB for 26 Sep 2020. Internet Traffic: Local: Edatel S.A data is updated daily, averaging 0.000 GB from Mar 2020 (Median) to 27 Sep 2020, with 182 observations. The data reached an all-time high of 0.000 GB in 27 Sep 2020 and a record low of 0.000 GB in 27 Sep 2020. Internet Traffic: Local: Edatel S.A data remains active status in CEIC and is reported by Communications Regulation Commission. The data is categorized under Global Database’s Colombia – Table CO.TB004: Internet Traffic: by Provider.

IP Transit Market Outlook

According to our latest research, the global IP Transit market size reached USD 6.7 billion in 2024, demonstrating robust growth driven by the surge in digital transformation, cloud adoption, and the proliferation of high-bandwidth applications. The market is expected to expand at a CAGR of 8.1% from 2025 to 2033, reaching a forecasted value of USD 12.5 billion by 2033. This strong growth trajectory is primarily fueled by increasing demand for reliable, high-capacity internet connectivity among enterprises, content providers, and data centers, as well as the continuous expansion of global internet infrastructure.

The primary growth factor for the IP Transit market is the exponential rise in global internet traffic, largely attributed to the widespread adoption of cloud computing, video streaming, online gaming, and other data-intensive applications. Enterprises are increasingly migrating mission-critical workloads to the cloud, necessitating robust and scalable IP Transit solutions to ensure seamless connectivity and minimal latency. Additionally, the rapid proliferation of IoT devices and the advent of 5G networks are driving the need for higher bandwidth and more reliable internet services, further boosting the demand for IP Transit. These trends are compelling service providers to enhance their infrastructure, invest in advanced networking technologies, and offer differentiated services to meet the evolving requirements of their customers.

Another significant growth driver is the expanding footprint of data centers and content delivery networks (CDNs) across the globe. As digital content consumption soars, content providers and OTT platforms are under pressure to deliver high-quality, uninterrupted streaming experiences. This has led to an increased reliance on IP Transit services to efficiently manage and route large volumes of data traffic across geographically dispersed locations. Enterprises and ISPs are also leveraging IP Transit to optimize network performance, reduce operational costs, and achieve greater flexibility in managing their internet traffic. The ongoing trend of network virtualization and software-defined networking (SDN) is further enabling service providers to offer more agile and scalable IP Transit solutions.

The competitive landscape of the IP Transit market is also being shaped by regulatory frameworks and government initiatives aimed at improving internet infrastructure and bridging the digital divide. In many regions, governments are investing in broadband expansion projects, fostering public-private partnerships, and incentivizing the deployment of high-speed internet services. These initiatives are opening up new growth avenues for IP Transit providers, particularly in emerging markets where internet penetration is still evolving. Furthermore, the increasing focus on cybersecurity and data privacy is prompting organizations to seek secure and resilient IP Transit solutions, driving innovation and differentiation among market players.

From a regional perspective, North America currently dominates the IP Transit market, accounting for the largest share in terms of revenue and infrastructure deployment. The region's advanced digital ecosystem, high internet penetration, and strong presence of leading technology companies are key contributors to its market leadership. However, Asia Pacific is emerging as the fastest-growing region, fueled by rapid urbanization, expanding internet user base, and significant investments in broadband infrastructure. Europe, Latin America, and the Middle East & Africa are also witnessing steady growth, supported by increasing digitalization and favorable regulatory environments. As market dynamics continue to evolve, regional players are focusing on strategic collaborations, capacity expansion, and technological innovation to capture new growth opportunities.

Service Type Analysis

The IP Transit market by service type is primarily segmented into Dedicated Internet Access (DIA) and Shared Internet Access (SIA), each catering to distinct customer requirements and network demands. Dedicated Internet Access offers exclusive, high-performance connectivity, ensuring consistent bandwidth and low latency, making it the preferred choice for large enterprises, data centers, and organizations with mission-critical operations. The demand for DIA is being driven by the increasing adoption of cloud-based applications, remote work

Traffic analytics, rankings, and competitive metrics for logos-world.net as of September 2025

As of July 2025, mobile phones accounted for **** percent of web page views in Saudi Arabia. The United Arab Emirates ranked second, with mobile devices generating approximately ***** percent of web traffic. Poland, Portugal, and Malaysia saw less than ** percent of their national internet traffic coming from mobile devices. Additionally, Russia ranked last for mobile internet traffic as of the middle of 2025, as ***** percent of the total internet traffic in the country came from smartphones and internet connected mobile devices.

These are similarity matrices of countries based on dfferent modalities of web use. Alexa website traffic, trending vidoes on Youtube and Twitter trends. Each matrix is a month of data aggregated

Introduction

Greetings to the ML community! We are excited to introduce our comprehensive Network Traffic Dataset, a resource that includes not only network packet data curated for machine learning applications, but also the original PCAP files from which the dataset was derived. This dataset was born out of a rigorous lab setup and is ideal for a variety of use cases such as intrusion detection, network performance analysis, anomaly detection, and more.

Lab Setup

The data is sourced from a controlled lab environment featuring three key systems:

1. Kali Machine (10.20.30.103): Used as the primary source of attack traffic in our lab setup, leveraging Kali's extensive set of penetration testing and attack tools.
2. OWASP Broken Web Application (10.20.30.101): Hosts a wide variety of intentionally insecure web applications, providing a target for our Kali machine's attack traffic.
3. Normal Windows PC (10.20.30.1): Operates as a source of benign traffic for our dataset, representing normal user activity on a network.

Dataset Creation

The creation of the PCAP file, its transformation into a CSV file, and the addition of an "alert" feature were carried out in a systematic manner. First, we set up a controlled lab environment featuring a Kali Machine, an OWASP Broken Web Application server, and a Normal Windows PC. We then captured the network traffic between these systems using a packet sniffer, which resulted in a comprehensive PCAP file encompassing a wide array of network protocols and traffic scenarios, both benign and suspicious.

To transform this raw packet data into a more accessible and machine-readable format, we used Tshark, a network protocol analyzer. Through Tshark's powerful extraction capabilities, we parsed the PCAP file and transformed it into a CSV file. Each row in this CSV file corresponds to a single network packet, and each column represents a specific field extracted from that packet.

The final step was the addition of an "**alert**" feature to the dataset. This feature was designed to assist machine learning researchers in their work, particularly in areas such as anomaly detection or intrusion detection. To create this feature, we labeled each network packet as either "benign" or "suspicious" based on its origin and nature. The "**benign**" label represents normal network traffic primarily from the Normal Windows PC, while the "**suspicious**" label signifies potential attack traffic mainly sourced from the Kali machine attacking the OWASP server. This addition of the "alert" feature provides an important target variable for supervised machine learning models.

Content

This dataset includes two distinct parts:

1. Original PCAP Files: The original PCAP files, which capture the raw network traffic between the systems in our lab setup, serve as the source of our dataset.
2. CSV Data: The CSV file contains data extracted from the PCAP files using Tshark, a network protocol analyzer. The extracted data includes a wide range of fields relevant to various protocols such as Ethernet, IP, TCP, UDP, ICMP, HTTP, DNS, SMTP, POP, IMAP, and FTP.

Each row in the CSV dataset corresponds to a single network packet, with each column representing one of the fields extracted from that packet. These fields capture a comprehensive view of each packet's metadata and content, providing an extensive base for network traffic analysis.

An additional column has been added to label each row as either "**benign**" or "**suspicious**". The "benign" label represents normal network traffic, primarily from the Normal Windows PC, while the "suspicious" label represents potential attack traffic, mainly sourced from the Kali machine attacking the OWASP server.

Field Descriptions

Let's go through each field:

1. frame.number: The number of the packet within the capture file.
2. frame.len: The length of the packet.
3. frame.time: The timestamp of when the packet was captured.
4. frame.time_epoch: The timestamp in seconds since the epoch (Jan 1, 1970) when the packet was captured.
5. frame.protocols: List of all protocols used in the packet.
6. eth.src: The source MAC address.
7. eth.dst: The destination MAC address.
8. eth.type: The type field of the Ethernet frame.
9. ip.src: The source IP address.
10. ip.dst: The destination IP address.
11. ip.len: The total length of the IP packet, including headers and data.
12. ip.ttl: The time-to-live value for the IP packet.
13. ip.flags: The flags set in the IP header.
14. ip.frag_offset: The fragmentation offset for the IP packet.
15. ip.proto: The protocol used in the IP packet.
16. ip.version: The version of the IP protocol used (IPv4 or IPv6).
17. ip.dsfield: The Differentiated Services Field (used for Quality of Service).
18. ip.checksum: The checksum of the IP header.
19. tcp.srcport: The source port...

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Global network traffic analytics Industry Overview

Technavio’s analysts have identified the increasing use of network traffic analytics solutions to be one of major factors driving market growth. With the rapidly changing IT infrastructure, security hackers can steal valuable information through various modes. With the increasing dependence on web applications and websites for day-to-day activities and financial transactions, the instances of theft have increased globally. Also, the emergence of social networking websites has aided the malicious attackers to extract valuable information from vulnerable users. The increasing consumer dependence on web applications and websites for day-to-day activities and financial transactions are further increasing the risks of theft. This encourages the organizations to adopt network traffic analytics solutions.

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Companies covered

The network traffic analytics market is fairly concentrated due to the presence of few established companies offering innovative and differentiated software and services. By offering a complete analysis of the competitiveness of the players in the network monitoring tools market offering varied software and services, this network traffic analytics industry analysis report will aid clients identify new growth opportunities and design new growth strategies.

The report offers a complete analysis of a number of companies including:

Allot
Cisco Systems
IBM
Juniper Networks
Microsoft
Symantec

Network traffic analytics market growth based on geographic regions

Americas
APAC
EMEA

With a complete study of the growth opportunities for the companies across regions such as the Americas, APAC, and EMEA, our industry research analysts have estimated that countries in the Americas will contribute significantly to the growth of the network monitoring tools market throughout the predicted period.

Network traffic analytics market growth based on end-user

Telecom
BFSI
Healthcare
Media and entertainment

According to our market research experts, the telecom end-user industry will be the major end-user of the network monitoring tools market throughout the forecast period. Factors such as increasing use of network traffic analytics solutions and increasing use of mobile devices at workplaces will contribute to the growth of the market shares of the telecom industry in the network traffic analytics market.

Key highlights of the global network traffic analytics market for the forecast years 2018-2022:

CAGR of the market during the forecast period 2018-2022
Detailed information on factors that will accelerate the growth of the network traffic analytics market during the next five years
Precise estimation of the global network traffic analytics market size and its contribution to the parent market
Accurate predictions on upcoming trends and changes in consumer behavior
Growth of the network traffic analytics industry across various geographies such as the Americas, APAC, and EMEA
A thorough analysis of the market’s competitive landscape and detailed information on several vendors
Comprehensive information about factors that will challenge the growth of network traffic analytics companies

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This market research report analyzes the market outlook and provides a list of key trends, drivers, and challenges that are anticipated to impact the global network traffic analytics market and its stakeholders over the forecast years.

The global network traffic analytics market analysts at Technavio have also considered how the performance of other related markets in the vertical will impact the size of this market till 2022. Some of the markets most likely to influence the growth of the network traffic analytics market over the coming years are the Global Network as a Service Market and the Global Data Analytics Outsourcing Market.

Technavio’s collection of market research reports offer insights into the growth of markets across various industries. Additionally, we also provide customized reports based on the specific requirement of our clients.

The global traffic control cabinet market, valued at $2.869 billion in 2025, is projected to experience steady growth, driven by increasing urbanization, rising investments in smart city infrastructure, and the growing need for efficient traffic management systems worldwide. The market's 3.8% CAGR indicates a consistent expansion through 2033, fueled by advancements in adaptive control cabinet technology offering improved traffic flow optimization and reduced congestion. Key application areas, such as urban transportation and public facilities, are experiencing significant growth, as cities worldwide prioritize improving road safety and traffic efficiency. The market is segmented by type, with timing control cabinets and adaptive control cabinets dominating the market share. Adaptive control cabinets are gaining traction due to their ability to dynamically adjust traffic signals based on real-time traffic conditions, thus optimizing traffic flow and reducing travel times. Major players in the market include SWARCO, Bison Profab, and others, competing on factors like technological innovation, product features, and geographical reach. While challenges remain, such as high initial investment costs for advanced systems and potential cybersecurity vulnerabilities, the long-term growth prospects remain positive due to ongoing government initiatives promoting smart city development and sustainable transportation solutions. The regional distribution of the market reflects global urbanization patterns, with North America and Europe holding substantial market shares due to advanced infrastructure and technological adoption. However, Asia-Pacific is expected to witness significant growth in the coming years driven by rapid urbanization and infrastructure development in countries like China and India. The competitive landscape is characterized by both established international players and regional manufacturers. The market is likely to see further consolidation through mergers and acquisitions as companies strive to expand their product portfolios and global reach. Technological advancements, such as the integration of artificial intelligence and the Internet of Things (IoT) in traffic management systems, are expected to drive innovation and shape the future of the traffic control cabinet market. The focus on sustainable and energy-efficient solutions is also a significant factor shaping the market's trajectory.

This is a dynamic traffic map service with capabilities for visualizing traffic speeds relative to free-flow speeds as well as traffic incidents which can be visualized and identified. The traffic data is updated every five minutes. Traffic speeds are displayed as a percentage of free-flow speeds, which is frequently the speed limit or how fast cars tend to travel when unencumbered by other vehicles. The streets are color coded as follows:Green (fast): 85 - 100% of free flow speedsYellow (moderate): 65 - 85%Orange (slow); 45 - 65%Red (stop and go): 0 - 45%Esri's historical, live, and predictive traffic feeds come directly from HERE (www.HERE.com). HERE collects billions of GPS and cell phone probe records per month and, where available, uses sensor and toll-tag data to augment the probe data collected. An advanced algorithm compiles the data and computes accurate speeds. Historical traffic is based on the average of observed speeds over the past three years. The live and predictive traffic data is updated every five minutes through traffic feeds. The color coded traffic map layer can be used to represent relative traffic speeds; this is a common type of a map for online services and is used to provide context for routing, navigation and field operations. The traffic map layer contains two sublayers: Traffic and Live Traffic. The Traffic sublayer (shown by default) leverages historical, live and predictive traffic data; while the Live Traffic sublayer is calculated from just the live and predictive traffic data only. A color coded traffic map image can be requested for the current time and any time in the future. A map image for a future request might be used for planning purposes. The map layer also includes dynamic traffic incidents showing the location of accidents, construction, closures and other issues that could potentially impact the flow of traffic. Traffic incidents are commonly used to provide context for routing, navigation and field operations. Incidents are not features; they cannot be exported and stored for later use or additional analysis. The service works globally and can be used to visualize traffic speeds and incidents in many countries. Check the service coverage web map to determine availability in your area of interest. In the coverage map, the countries color coded in dark green support visualizing live traffic. The support for traffic incidents can be determined by identifying a country. For detailed information on this service, including a data coverage map, visit the directions and routing documentation and ArcGIS Help.