Daily utilization metrics for data.lacity.org and geohub.lacity.org. Updated monthly

Explore our detailed website traffic dataset featuring key metrics like page views, session duration, bounce rate, traffic source, and conversion rates.

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.

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

This dataset was used in the Kaggle Wikipedia Web Traffic forecasting competition. It contains 145063 daily time series representing the number of hits or web traffic for a set of Wikipedia pages from 2015-07-01 to 2017-09-10.

The original dataset contains missing values. They have been simply replaced by zeros.

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As of the second quarter of 2025, ***** percent of web traffic in the United States originated from mobile devices, down from over ** percent in the last quarter of 2024. In comparison, over ********** of web traffic worldwide was generated via mobile in the last examined period.

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.

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:

Snapshot img

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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See the complete table of contents and list of exhibits, as well as selected illustrations and example pages from this report.

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.

Documentation for Network Traffic Dataset

Dataset Overview

This dataset consists of network traffic captured from a Kali Linux machine, aimed at helping the development and evaluation of machine learning models for distinguishing between normal and malicious (specifically flood attack) network activities. It includes a variety of features essential for identifying potential cybersecurity threats alongside labels indicating whether each packet is part of flood traffic.

Data Collection Methodology

The dataset was carefully compiled using network traffic captured from a dedicated Kali Linux setup. The capture environment consisted of a Kali Linux machine configured to generate and capture both normal and malicious network traffic and a target machine running a Windows OS to simulate a real-world network environment.

Traffic Generation:

Normal Traffic: Involved routine network activities such as web browsing and pinging between the Kali Linux machine and the Windows machine.

Malicious Traffic: Utilized hping3 to simulate flood attacks, specifically ICMP flood attacks, targeting the Windows machine from the Kali Linux machine [1].

Capture Process: Wireshark was used on the Kali Linux machine to capture all incoming and outgoing network traffic [2]. The capture was set up to record detailed packet information, including timestamps, source and destination IP addresses, ports, and protocols. The captures were conducted with careful monitoring to precisely mark the start and end times of the flood attack for accurate dataset labeling.

Dataset Description

The dataset is a CSV file containing a comprehensive collection of network traffic packets labeled to distinguish between normal and malicious traffic. It includes the following columns:

Timestamp: The capture time of each packet, providing insights into the traffic flow and enabling analysis of traffic patterns over time. Source IP Address: Identifies the origin of the packet, crucial for pinpointing potential sources of attacks. Destination IP Address: Indicates the packet's intended recipient, useful for identifying targeted resources. Source Port and Destination Port: Offer insights into the services involved in the communication. Protocol: Specifies the protocol used, such as TCP, UDP, or ICMP, essential for analyzing the nature of the traffic. Length: The size of the packet in bytes, which can signal unusual traffic patterns often associated with malicious activities. bad_packet: A binary label with 1 indicating traffic identified as part of a flood attack and 0 denoting normal traffic. Precise timestamps marking the start and end of flood attacks were used to accurately label this column. Packets captured within these defined intervals were marked as malicious (bad_packet = 1), whereas all others were considered normal traffic. Python and Pandas were used for the labeling process [3][4].

Potential Applications

a. Intrusion Detection Systems (IDS): The dataset can be used in training models to enhance IDS capabilities, enabling more effective detection of flood-based network attacks. b. Network Traffic Monitoring: Tools making use of machine learning can leverage the dataset for more accurate network traffic monitoring, identifying and alerting suspicious activities in real time. c. Cybersecurity Training: Educational institutions and training programs can use the dataset to provide practical experience in machine learning-based threat detection.

Proposed Machine Learning Technique: Supervised Machine Learning, specifically Deep Learning with Convolutional Neural Networks (CNNs).

CNNs, even though it is usually used for image processing, have shown promise in analyzing sequential data. The spatial hierarchy in network packets (from individual bytes to overall packet structure) can be analogous to the patterns CNNs excel at identifying. Utilizing CNNs could allow for the extraction of complex data in network traffic that indicate malicious activities, improving detection accuracy beyond traditional methods.

Conclusion

This dataset represents a significant step towards using machine learning for cybersecurity, specifically in the field of intrusion detection and network monitoring. By providing a detailed and accurately labeled dataset of normal and malicious network traffic, it lays the groundwork for developing complex models capable of identifying and mitigating flood attacks in real-time. In the future, we could include a broader range of attack types and more traffic patterns, further enhancing the dataset's utility and the effectiveness of models trained on it.

References [1] https://linux.die.net/man/8/hping3 [2] https://www.wireshark.org/docs/ [3] https://pandas.pydata.org/docs/ [4] https://docs.python.org/3/tutorial/index.html

Real-time website visitor analytics and digital engagement metrics across global markets for institutional investment research and competitive intelligence.

Context

The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website.

Content

The sample dataset contains Google Analytics 360 data from the Google Merchandise Store, a real ecommerce store. The Google Merchandise Store sells Google branded merchandise. The data is typical of what you would see for an ecommerce website. It includes the following kinds of information:

Traffic source data: information about where website visitors originate. This includes data about organic traffic, paid search traffic, display traffic, etc. Content data: information about the behavior of users on the site. This includes the URLs of pages that visitors look at, how they interact with content, etc. Transactional data: information about the transactions that occur on the Google Merchandise Store website.

Fork this kernel to get started.

Acknowledgements

Data from: https://bigquery.cloud.google.com/table/bigquery-public-data:google_analytics_sample.ga_sessions_20170801

Banner Photo by Edho Pratama from Unsplash.

Inspiration

What is the total number of transactions generated per device browser in July 2017?

The real bounce rate is defined as the percentage of visits with a single pageview. What was the real bounce rate per traffic source?

What was the average number of product pageviews for users who made a purchase in July 2017?

What was the average number of product pageviews for users who did not make a purchase in July 2017?

What was the average total transactions per user that made a purchase in July 2017?

What is the average amount of money spent per session in July 2017?

What is the sequence of pages viewed?

Network Traffic Analyzer Market size was valued at USD 3.54 Billion in 2024 and is projected to reach USD 5.86 Billion by 2032, growing at a CAGR of 10.6% during the forecast period 2026-2032.

Global Network Traffic Analyzer Market Drivers

The market drivers for the Network Traffic Analyzer Market can be influenced by various factors. These may include:

Growing Risks to Cybersecurity: The increasing sophistication and frequency of cyber threats and attacks are driving the need for network traffic analyzers to improve security protocols. These instruments support the identification and mitigation of dubious network activity. Increasing Network Infrastructure Complexity: Organisations need sophisticated tools to monitor and analyze network traffic because network infrastructures, especially hybrid and multi-cloud systems, are becoming more and more complicated. Network traffic analyzers shed light on these complex infrastructures' security and performance. Growing Cloud Computing Adoption: There is a growing need for network traffic analyzers that can monitor and optimize performance across cloud environments due to the widespread adoption of cloud services and the migration of applications and data to the cloud.

The global Network Traffic Monitoring market is experiencing robust growth, driven by the increasing adoption of cloud computing, the proliferation of IoT devices, and the rising need for enhanced network security. The market, estimated at $15 billion in 2025, is projected to achieve a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching approximately $45 billion by 2033. Key drivers include the imperative for businesses to optimize network performance, ensure compliance with data regulations, and proactively mitigate cyber threats. The growing complexity of network infrastructures, fueled by digital transformation initiatives across various sectors—including IT services, telecommunications, and banking—is significantly contributing to this growth. Furthermore, the transition to hybrid and multi-cloud environments necessitates sophisticated network traffic monitoring solutions capable of providing comprehensive visibility and control across diverse network topologies. While the initial investment in network monitoring solutions can be a restraint for smaller organizations, the long-term cost savings in terms of improved network efficiency and reduced downtime often outweigh these initial costs. The market segmentation reveals a strong demand for both SaaS and PaaS solutions, with SaaS gaining popularity due to its ease of deployment and scalability. Geographically, North America currently holds the largest market share, followed by Europe and Asia Pacific. However, the Asia Pacific region is anticipated to demonstrate the fastest growth rate during the forecast period, driven by increasing digitalization and infrastructure development in countries like China and India. The competitive landscape is marked by a mix of established players like Cisco Systems and IBM, and emerging vendors offering innovative solutions. The market’s future growth will be significantly shaped by advancements in AI-powered analytics, the integration of network traffic monitoring with security information and event management (SIEM) systems, and the increasing demand for real-time network visibility and anomaly detection. The continued evolution of network technologies and the rise of 5G will further fuel the demand for advanced network traffic monitoring tools.

The global Network Traffic Monitoring market is experiencing robust growth, driven by the escalating demand for enhanced network visibility and security in an increasingly interconnected world. The market, currently valued at approximately $15 billion in 2025, is projected to witness a Compound Annual Growth Rate (CAGR) of 12% from 2025 to 2033, reaching an estimated market size of $45 billion by 2033. This expansion is fueled by several key factors, including the proliferation of cloud computing, the rise of the Internet of Things (IoT), and the increasing prevalence of sophisticated cyber threats. Businesses across various sectors, including IT services, telecommunications, and banking, are adopting network traffic monitoring solutions to optimize network performance, enhance security posture, and ensure regulatory compliance. The market is segmented by deployment model (SaaS and PaaS) and application (IT Services, Telecommunications, Banking, and Others), each contributing uniquely to the overall market growth. The increasing adoption of cloud-based solutions and the demand for advanced analytics are also major catalysts. The competitive landscape is characterized by the presence of established players like Cisco Systems, IBM, and SolarWinds, alongside emerging technology providers such as Auvik and ManageEngine. These companies are continually innovating to offer advanced features like AI-powered anomaly detection, real-time threat intelligence, and enhanced network visualization capabilities. The geographical distribution of the market shows strong growth across North America and Europe, driven by high technological adoption rates and stringent data security regulations. However, Asia-Pacific is expected to witness significant growth in the coming years, fueled by rapid digital transformation and infrastructure development in countries like India and China. Market restraints include the complexity of implementing and managing these solutions, along with the high initial investment costs. However, the long-term benefits of improved network efficiency and enhanced security are overriding these concerns, leading to a sustained and positive market trajectory.

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source port and destination port

This is the aggregated version of the daily dataset used in the Kaggle Wikipedia Web Traffic forecasting competition. It contains 145063 time series representing the number of hits or web traffic for a set of Wikipedia pages from 2015-07-01 to 2017-09-05, after aggregating them into weekly.

The original dataset contains missing values. They have been simply replaced by zeros before aggregation.

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