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.

In November 2024, Google.com was the most popular website worldwide with 136 billion average monthly visits. The online platform has held the top spot as the most popular website since June 2010, when it pulled ahead of Yahoo into first place. Second-ranked YouTube generated more than 72.8 billion monthly visits in the measured period. The internet leaders: search, social, and e-commerce Social networks, search engines, and e-commerce websites shape the online experience as we know it. While Google leads the global online search market by far, YouTube and Facebook have become the world’s most popular websites for user generated content, solidifying Alphabet’s and Meta’s leadership over the online landscape. Meanwhile, websites such as Amazon and eBay generate millions in profits from the sale and distribution of goods, making the e-market sector an integral part of the global retail scene. What is next for online content? Powering social media and websites like Reddit and Wikipedia, user-generated content keeps moving the internet’s engines. However, the rise of generative artificial intelligence will bring significant changes to how online content is produced and handled. ChatGPT is already transforming how online search is performed, and news of Google's 2024 deal for licensing Reddit content to train large language models (LLMs) signal that the internet is likely to go through a new revolution. While AI's impact on the online market might bring both opportunities and challenges, effective content management will remain crucial for profitability on the web.

In January 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 kind of platforms for business and advertising purposes.

This data set contains internet traffic data captured by an Internet Service Provider (ISP) using Mikrotik SDN Controller and packet sniffer tools. The data set includes traffic from over 2000 customers who use Fibre to the Home (FTTH) and Gpon internet connections. The data was collected over a period of several months and contains all traffic in its original format with headers and packets.

The data set contains information on inbound and outbound traffic, including web browsing, email, file transfers, and more. The data set can be used for research in areas such as network security, traffic analysis, and machine learning.

**Data Collection Method: ** The data was captured using Mikrotik SDN Controller and packet sniffer tools. These tools capture traffic data by monitoring network traffic in real-time. The data set contains all traffic data in its original format, including headers and packets.

**Data Set Content: ** The data set is provided in a CSV format and includes the following fields:

1. Timestamp: The date and time the traffic was captured
2. Source IP Address: The IP address of the device that sent the traffic Destination IP Address: The IP address of the device that received the traffic Protocol: The network protocol used for the traffic (e.g. TCP, UDP) Source Port: The port used by the source device for the traffic Destination Port: The port used by the destination device for the traffic Packet Size: The size of the packet in bytes Payload: The payload data of the packet The data set contains a large volume of traffic data from over 2000 customers. The data is organized by timestamp and includes all traffic data in its original format, including headers and packets. The data set contains both inbound and outbound traffic, and covers various types of internet traffic, including web browsing, email, file transfers, and more. one of listed protocols: ipsec-ah - IPsec AH protocol *ipsec-esp - IPsec ESP protocol ddp - datagram delivery protocol egp - exterior gateway protocol ggp - gateway-gateway protocol gre - general routing encapsulation hmp - host monitoring protocol idpr-cmtp - idpr control message transport icmp - internet control message protocol icmpv6 - internet control message protocol v6 igmp - internet group management protocol ipencap - ip encapsulated in ip ipip - ip encapsulation encap - ip encapsulation iso-tp4 - iso transport protocol class 4 ospf - open shortest path first pup - parc universal packet protocol pim - protocol independent multicast rspf - radio shortest path first rdp - reliable datagram protocol st - st datagram mode tcp - transmission control protocol udp - user datagram protocol vmtp - versatile message transport vrrp - virtual router redundancy protocol xns-idp - xerox xns idp xtp - xpress transfer protocol

MAC Protocol Examples 802.2 - 802.2 Frames (0x0004) arp - Address Resolution Protocol (0x0806) homeplug-av - HomePlug AV MME (0x88E1) ip - Internet Protocol version 4 (0x0800) ipv6 - Internet Protocol Version 6 (0x86DD) ipx - Internetwork Packet Exchange (0x8137) lldp - Link Layer Discovery Protocol (0x88CC) loop-protect - Loop Protect Protocol (0x9003) mpls-multicast - MPLS multicast (0x8848) mpls-unicast - MPLS unicast (0x8847) packing-compr - Encapsulated packets with compressed IP packing (0x9001) packing-simple - Encapsulated packets with simple IP packing (0x9000) pppoe - PPPoE Session Stage (0x8864) pppoe-discovery - PPPoE Discovery Stage (0x8863) rarp - Reverse Address Resolution Protocol (0x8035) service-vlan - Provider Bridging (IEEE 802.1ad) & Shortest Path Bridging IEEE 802.1aq (0x88A8) vlan - VLAN-tagged frame (IEEE 802.1Q) and Shortest Path Bridging IEEE 802.1aq with NNI compatibility (0x8100)

**Data Usage: ** The data set can be used for research in areas such as network security, traffic analysis, and machine learning. Researchers can use the data to develop new algorithms for detecting and preventing cyber attacks, analyzing internet traffic patterns, and more.

**Data Availability: ** If you are interested in using this data set for research purposes, please contact us at asfandyar250@gmail.com for more information and references. The data set is available for download on Kaggle and can be accessed by researchers who have obtained permission from the ISP.

We hope this data set will be useful for researchers in the field of network security and traffic analysis. If you have any questions or need further information, please do not hesitate to contact us. https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F5985737%2F61c81ce9eb393f8fc7c15540c9819b95%2FData.PNG?generation=1683750473536727&alt=media" alt=""> You can use Wireshark or other software's to view files

The dataset is a set of network traffic traces in pcap/csv format captured from a single user. The traffic is classified in 5 different activities (Video, Bulk, Idle, Web, and Interactive) and the label is shown in the filename. There is also a file (mapping.csv) with the mapping of the host's IP address, the csv/pcap filename and the activity label.

Activities:

Interactive: applications that perform real-time interactions in order to provide a suitable user experience, such as editing a file in google docs and remote CLI's sessions by SSH. Bulk data transfer: applications that perform a transfer of large data volume files over the network. Some examples are SCP/FTP applications and direct downloads of large files from web servers like Mediafire, Dropbox or the university repository among others. Web browsing: contains all the generated traffic while searching and consuming different web pages. Examples of those pages are several blogs and new sites and the moodle of the university. Vídeo playback: contains traffic from applications that consume video in streaming or pseudo-streaming. The most known server used are Twitch and Youtube but the university online classroom has also been used. Idle behaviour: is composed by the background traffic generated by the user computer when the user is idle. This traffic has been captured with every application closed and with some opened pages like google docs, YouTube and several web pages, but always without user interaction.

The capture is performed in a network probe, attached to the router that forwards the user network traffic, using a SPAN port. The traffic is stored in pcap format with all the packet payload. In the csv file, every non TCP/UDP packet is filtered out, as well as every packet with no payload. The fields in the csv files are the following (one line per packet): Timestamp, protocol, payload size, IP address source and destination, UDP/TCP port source and destination. The fields are also included as a header in every csv file.

The amount of data is stated as follows:

Bulk : 19 traces, 3599 s of total duration, 8704 MBytes of pcap files Video : 23 traces, 4496 s, 1405 MBytes Web : 23 traces, 4203 s, 148 MBytes Interactive : 42 traces, 8934 s, 30.5 MBytes Idle : 52 traces, 6341 s, 0.69 MBytes

The code of our machine learning approach is also included. There is a README.txt file with the documentation of how to use the code.

As of March 2024, Google.com accounted for over 7.35 percent of referral traffic to Facebook.com. Facebook's second-largest referral traffic driver was msn.com, which generated 4.83 percent of referral traffic to the social media platform. Overall, referrals accounted for slightly less than two percent of traffic to Facebook.

The people from Czech are publishing a dataset for the HTTPS traffic classification.

Since the data were captured mainly in the real backbone network, they omitted IP addresses and ports. The datasets consist of calculated from bidirectional flows exported with flow probe Ipifixprobe. This exporter can export a sequence of packet lengths and times and a sequence of packet bursts and time. For more information, please visit ipfixprobe repository (Ipifixprobe).

During research, they divided HTTPS into five categories: L -- Live Video Streaming, P -- Video Player, M -- Music Player, U -- File Upload, D -- File Download, W -- Website, and other traffic.

They have chosen the service representatives known for particular traffic types based on the Alexa Top 1M list and Moz's list of the most popular 500 websites for each category. They also used several popular websites that primarily focus on the audience in Czech. The identified traffic classes and their representatives are provided below:

Live Video Stream Twitch, Czech TV, YouTube Live Video Player DailyMotion, Stream.cz, Vimeo, YouTube Music Player AppleMusic, Spotify, SoundCloud File Upload/Download FileSender, OwnCloud, OneDrive, Google Drive Website and Other Traffic Websites from Alexa Top 1M list

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.

This is a dataset of Tor cell file extracted from browsing simulation using Tor Browser. The simulations cover both desktop and mobile webpages. The data collection process was using WFP-Collector tool (https://github.com/irsyadpage/WFP-Collector). All the neccessary configuration to perform the simulation as detailed in the tool repository.The webpage URL is selected by using the first 100 website based on: https://dataforseo.com/free-seo-stats/top-1000-websites.Each webpage URL is visited 90 times for each deskop and mobile browsing mode.

According to a report published by DataReportal, as of December 2024, mobile phones had the highest share of web traffic at ***** percent in Thailand. By comparison, the share of web traffic on laptops and desktop computers was around ** percent.

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The global website visitor tracking software market is experiencing robust growth, driven by the increasing need for businesses to understand online customer behavior and optimize their digital strategies. The market, estimated at $5 billion in 2025, is projected to expand at a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching approximately $15 billion by 2033. This expansion is fueled by several key factors, including the rising adoption of digital marketing strategies, the growing importance of data-driven decision-making, and the increasing sophistication of website visitor tracking tools. Cloud-based solutions dominate the market due to their scalability, accessibility, and cost-effectiveness, particularly appealing to Small and Medium-sized Enterprises (SMEs). However, large enterprises continue to invest significantly in on-premise solutions for enhanced data security and control. The market is highly competitive, with numerous established players and emerging startups offering a range of features and functionalities. Technological advancements, such as AI-powered analytics and enhanced integration with other marketing tools, are shaping the future of the market. The market's geographical distribution reflects the global digital landscape. North America, with its mature digital economy and high adoption rates, holds a significant market share. However, regions like Asia-Pacific are showing rapid growth, driven by increasing internet penetration and digitalization across various industries. Despite the overall positive outlook, challenges such as data privacy regulations and the increasing complexity of website tracking technology are influencing market dynamics. The ongoing competition among vendors necessitates continuous innovation and the development of more user-friendly and insightful tools. The future growth of the website visitor tracking software market is promising, fueled by the continuing importance of data-driven decision-making within marketing and business strategies. A key factor will be the ongoing adaptation to evolving privacy regulations and user expectations.

Network Traffic Analysis Solutions Market Outlook

The global network traffic analysis solutions market size was estimated at USD 3.5 billion in 2023 and is projected to reach USD 9.8 billion by 2032, reflecting a compound annual growth rate (CAGR) of 12.1%. This substantial growth is largely driven by the increasing demand for robust cybersecurity measures across various sectors. With an ever-growing volume of network traffic due to the proliferation of connected devices and the adoption of digital transformation initiatives, organizations are compelled to deploy sophisticated traffic analysis tools to effectively monitor, manage, and secure their networks. The expansion of cloud services, coupled with the rise in cyber threats, further accentuates the need for advanced traffic analysis capabilities.

The surge in cyber threats, including sophisticated hacking techniques and ransomware attacks, has become a pivotal growth factor for the network traffic analysis solutions market. As organizations strive to protect sensitive data and ensure the integrity of their networks, there is a heightened demand for solutions that can provide real-time visibility and control over network traffic. This growing emphasis on cybersecurity is not limited to large enterprises but is increasingly becoming a priority for small and medium enterprises (SMEs) as well. Consequently, the increasing cyber threat landscape is stimulating the adoption of network traffic analysis solutions across different organizational sizes, driving market growth.

Moreover, the rise of Internet of Things (IoT) devices is significantly contributing to the increased need for network traffic analysis. IoT devices generate vast amounts of data that need to be managed effectively to prevent network congestion and potential security breaches. By leveraging traffic analysis solutions, organizations can optimize IoT device performance and ensure seamless data flow while maintaining robust security protocols. As the IoT ecosystem continues to expand, it is expected to further fuel the demand for network traffic analysis solutions, facilitating better management and security of network resources.

In addition to cybersecurity concerns and IoT proliferation, regulatory compliance is another critical growth driver for the network traffic analysis solutions market. Organizations across various industries, such as BFSI, healthcare, and government sectors, are under increasing pressure to comply with stringent data protection regulations. Network traffic analysis solutions help these organizations monitor compliance effectively by providing detailed insights into network activity and data flows. As regulations continue to evolve and become more complex, the role of network traffic analysis solutions in ensuring compliance and mitigating risks is expected to become increasingly important, further bolstering market growth.

Network Telemetry Solutions are becoming increasingly essential in the realm of network traffic analysis. These solutions provide real-time data collection and analysis, enabling organizations to gain deeper insights into their network operations. By leveraging network telemetry, businesses can proactively identify and address potential issues before they escalate into significant problems. This capability is particularly valuable in today's fast-paced digital environment, where network performance and security are critical to maintaining operational efficiency. As the demand for more granular visibility into network activities grows, network telemetry solutions are poised to play a pivotal role in enhancing the capabilities of traffic analysis tools, offering a more comprehensive approach to network management and security.

From a regional perspective, North America is anticipated to maintain a dominant position in the network traffic analysis solutions market. This can be attributed to the presence of major technology companies, a high adoption rate of advanced technologies, and stringent cybersecurity regulations. The region's established digital infrastructure and focus on innovation also contribute to market growth. Meanwhile, the Asia Pacific region is projected to witness the highest growth rate due to rapid digitalization, increasing internet penetration, and growing investments in IT infrastructure. As businesses in this region continue to adopt digital technologies and face rising cyber threats, the demand for network traffic analysis solutions is expected to surge significantly.

Component Analysis</h2

Web traffic statistics for the top 2000 most visited pages on nyc.gov by month.

Mobile Data Traffic Market Outlook

The global mobile data traffic market size was estimated at approximately USD 68 billion in 2023 and is projected to surge to about USD 320 billion by 2032, exhibiting a remarkable compound annual growth rate (CAGR) of 18.5% over the forecast period. This growth is driven by the increasing penetration of smartphones, advancements in network technologies, and the rising consumption of data-intensive applications and services.

One of the primary growth factors for the mobile data traffic market is the rapid expansion of the smartphone user base globally. As smartphones become more affordable and accessible, especially in emerging markets, the number of mobile internet users is skyrocketing. This trend is further amplified by the increasing availability of high-speed mobile networks, which make data-heavy applications such as video streaming and online gaming more feasible and attractive to users. The proliferation of affordable data plans is also encouraging users to consume more mobile data, thereby bolstering market growth.

Another significant driver of growth is the continuous evolution of network technologies. The transition from 3G to 4G, and now to 5G, has significantly enhanced data transmission speeds and network capabilities. 5G technology, in particular, promises ultra-low latency, higher capacity, and faster download and upload speeds, which are expected to revolutionize various sectors such as healthcare, automotive, and smart cities. The deployment and adoption of 5G networks are anticipated to boost mobile data traffic volumes exponentially, as it facilitates the seamless use of high-bandwidth applications, including augmented reality (AR), virtual reality (VR), and Internet of Things (IoT) devices.

The increase in video content consumption is also a major factor driving the market. Video traffic accounts for a substantial portion of mobile data usage, driven by platforms like YouTube, Netflix, and social media sites that prioritize video content. The trend of live streaming and video-on-demand services is creating a massive surge in data traffic, with users increasingly accessing high-definition (HD) and even 4K content. Moreover, the COVID-19 pandemic has accelerated the adoption of digital entertainment and online education, further increasing the demand for mobile data.

Regionally, the growth of mobile data traffic is witnessing variations with Asia Pacific leading the charge. The region's high population density, coupled with increasing urbanization and smartphone penetration, makes it a significant contributor to global data traffic. Countries like China and India are at the forefront, driven by government initiatives to promote digitalization and the rollout of advanced mobile networks. North America and Europe are also substantial markets due to their well-established network infrastructure and early adoption of new technologies. However, the growth rates in these regions are relatively moderate compared to the exponential growth seen in Asia Pacific and Latin America.

Traffic Type Analysis

The mobile data traffic market can be segmented by traffic type into video, audio, data, and others. Video traffic is the most dominant segment, accounting for the largest share of mobile data usage worldwide. The proliferation of video streaming services, alongside user-generated video content on social media platforms, significantly contributes to this dominance. As more users switch to high-definition and 4K streaming, the demand for data-intensive video content continues to rise. Additionally, the growing popularity of live streaming and video calls, particularly in the context of remote work and online education, further propels this segment's growth.

Audio traffic also plays a significant role in the mobile data traffic market. The increasing usage of music streaming services such as Spotify, Apple Music, and various podcast platforms are driving the growth of this segment. The trend of consuming audio content on the go, facilitated by improved network speeds and unlimited data plans, is contributing to a steady rise in mobile data traffic from audio services. Furthermore, the adoption of smart speakers and voice assistant technologies is expected to continue bolstering this segment.

Data traffic, encompassing all forms of non-visual and non-audio data, is another crucial segment. This includes browsing, app usage, emails, and other types of data transmission over mobile networks. With the increasing reliance on mobile applications for a wide array of activities—ra

Our Web Data dataset includes such data points as company name, location, headcount, industry, and size, among others. It offers extensive fresh and historical data, including even companies that operate in stealth mode.

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With millions of companies worldwide, Web Company Database helps you filter potential clients based on custom criteria and speed up the conversion process.

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We recommend B2B Web Data for investors to discover and evaluate businesses with the highest potential.

Gain strategic business insights, enhance decision-making, and maintain algorithms that signal investment opportunities with Coresignal’s global B2B Web Dataset.

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amazon.com is ranked #3 in US with 2.82B Traffic. Categories: Online Services. Learn more about website traffic, market share, and more!

Top 25 Daily Page Views for the main website of Los Angeles

IoT-23 is a dataset of network traffic from Internet of Things (IoT) devices. It has 20 malware captures executed in IoT devices, and 3 captures for benign IoT devices traffic. It was first published in January 2020, with captures ranging from 2018 to 2019. These IoT network traffic was captured in the Stratosphere Laboratory, AIC group, FEL, CTU University, Czech Republic. Its goal is to offer a large dataset of real and labeled IoT malware infections and IoT benign traffic for researchers to develop machine learning algorithms. This dataset and its research was funded by Avast Software. The malware was allow to connect to the Internet.

Dataset

This dataset was created by DNS_dataset

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