In 2024, most of the global website traffic was still generated by humans, but bot traffic is constantly growing. Fraudulent traffic through bad bot actors accounted for 37 percent of global web traffic in the most recently measured period, representing an increase of 12 percent from the previous year. Sophistication of Bad Bots on the rise The complexity of malicious bot activity has dramatically increased in recent years. Advanced bad bots have doubled in prevalence over the past 2 years, indicating a surge in the sophistication of cyber threats. Simultaneously, the share of simple bad bots drastically increased over the last years, suggesting a shift in the landscape of automated threats. Meanwhile, areas like food and groceries, sports, gambling, and entertainment faced the highest amount of advanced bad bots, with more than 70 percent of their bot traffic affected by evasive applications. Good and bad bots across industries The impact of bot traffic varies across different sectors. Bad bots accounted for over 50 percent of the telecom and ISPs, community and society, and computing and IT segments web traffic. However, not all bot traffic is considered bad. Some of these applications help index websites for search engines or monitor website performance, assisting users throughout their online search. Therefore, areas like entertainment, food and groceries, and even areas targeted by bad bots themselves experienced notable levels of good bot traffic, demonstrating the diverse applications of benign automated systems across different sectors.

Context

This file contains 5 years of daily time series data for several measures of traffic on a statistical forecasting teaching notes website whose alias is statforecasting.com. The variables have complex seasonality that is keyed to the day of the week and to the academic calendar. The patterns you you see here are similar in principle to what you would see in other daily data with day-of-week and time-of-year effects. Some good exercises are to develop a 1-day-ahead forecasting model, a 7-day ahead forecasting model, and an entire-next-week forecasting model (i.e., next 7 days) for unique visitors.

Content

The variables are daily counts of page loads, unique visitors, first-time visitors, and returning visitors to an academic teaching notes website. There are 2167 rows of data spanning the date range from September 14, 2014, to August 19, 2020. A visit is defined as a stream of hits on one or more pages on the site on a given day by the same user, as identified by IP address. Multiple individuals with a shared IP address (e.g., in a computer lab) are considered as a single user, so real users may be undercounted to some extent. A visit is classified as "unique" if a hit from the same IP address has not come within the last 6 hours. Returning visitors are identified by cookies if those are accepted. All others are classified as first-time visitors, so the count of unique visitors is the sum of the counts of returning and first-time visitors by definition. The data was collected through a traffic monitoring service known as StatCounter.

Inspiration

This file and a number of other sample datasets can also be found on the website of RegressIt, a free Excel add-in for linear and logistic regression which I originally developed for use in the course whose website generated the traffic data given here. If you use Excel to some extent as well as Python or R, you might want to try it out on this dataset.

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.

A collection of Web (HTTP) requests for the month of November 2009 originating from Indiana Univesity.

This dataset was used in the Data Visualization Challenge at WebSci 2014 in Bloomington, Indiana. It is a sample of the larger Indiana University Click dataset.

General data recollected for the studio " Analysis of the Quantitative Impact of Social Networks on Web Traffic of Cybermedia in the 27 Countries of the European Union". Four research questions are posed: what percentage of the total web traffic generated by cybermedia in the European Union comes from social networks? Is said percentage higher or lower than that provided through direct traffic and through the use of search engines via SEO positioning? Which social networks have a greater impact? And is there any degree of relationship between the specific weight of social networks in the web traffic of a cybermedia and circumstances such as the average duration of the user's visit, the number of page views or the bounce rate understood in its formal aspect of not performing any kind of interaction on the visited page beyond reading its content? To answer these questions, we have first proceeded to a selection of the cybermedia with the highest web traffic of the 27 countries that are currently part of the European Union after the United Kingdom left on December 31, 2020. In each nation we have selected five media using a combination of the global web traffic metrics provided by the tools Alexa (https://www.alexa.com/), which ceased to be operational on May 1, 2022, and SimilarWeb (https:// www.similarweb.com/). We have not used local metrics by country since the results obtained with these first two tools were sufficiently significant and our objective is not to establish a ranking of cybermedia by nation but to examine the relevance of social networks in their web traffic. In all cases, cybermedia whose property corresponds to a journalistic company have been selected, ruling out those belonging to telecommunications portals or service providers; in some cases they correspond to classic information companies (both newspapers and televisions) while in others they refer to digital natives, without this circumstance affecting the nature of the research proposed. Below we have proceeded to examine the web traffic data of said cybermedia. The period corresponding to the months of October, November and December 2021 and January, February and March 2022 has been selected. We believe that this six-month stretch allows possible one-time variations to be overcome for a month, reinforcing the precision of the data obtained. To secure this data, we have used the SimilarWeb tool, currently the most precise tool that exists when examining the web traffic of a portal, although it is limited to that coming from desktops and laptops, without taking into account those that come from mobile devices, currently impossible to determine with existing measurement tools on the market. It includes: Web traffic general data: average visit duration, pages per visit and bounce rate Web traffic origin by country Percentage of traffic generated from social media over total web traffic Distribution of web traffic generated from social networks Comparison of web traffic generated from social netwoks with direct and search procedures

As many general retailers or mass distribution channels experienced an exponential growth during the months of the COVID-19 induced lockdown in France, the source wanted to measure the total number of backlinks on the different retailers websites. Thus, Carrefour.fr was the leading general retailer with the most backlinks amounting to ***** on their website. A strategy of acquiring backlinks which therefore seems to be paying off for the major retailer, which drew around ***** percent of its overall traffic through this means.

Host country of organization for 86 websites in study.

Network Traffic Analysis (NTA) Software Market Outlook

The global Network Traffic Analysis (NTA) Software market size is poised to witness a robust growth trajectory, with a projected market valuation rising from approximately USD 3.5 billion in 2023 to an impressive USD 12.4 billion by 2032, growing at a compound annual growth rate (CAGR) of 15.2% during the forecast period. The surge in this market is predominantly fueled by the increasing need for sophisticated cybersecurity measures due to the escalating frequency and complexity of cyber threats. Organizations are progressively recognizing the critical importance of NTA software in detecting, monitoring, and responding to potential network anomalies and threats, driving the market's expansion.

A major growth factor contributing to the burgeoning NTA Software market is the exponential growth in data traffic, attributed to the widespread adoption of cloud computing, IoT devices, and the ongoing digital transformation across industries. As enterprises expand their digital footprint, the volume of data traversing networks has seen an unprecedented rise, necessitating advanced network traffic analysis solutions to ensure efficient management and security of data. Moreover, the increasing sophistication of cyber threats, including advanced persistent threats (APTs) and ransomware, has made continuous network monitoring and analysis indispensable for organizations striving to protect sensitive information and maintain business continuity.

Another significant driver for the NTA Software market is the growing regulatory pressures and compliance requirements across various sectors, including BFSI, healthcare, and government. These regulations mandate organizations to implement robust cybersecurity frameworks and ensure data protection, thereby propelling the demand for comprehensive NTA solutions. Companies are increasingly investing in NTA software to comply with standards such as GDPR, HIPAA, and PCI-DSS, which emphasize the importance of network security and data privacy. As regulatory landscapes continue to evolve, the necessity for effective network traffic analysis tools becomes even more pronounced, further accelerating market growth.

The increasing adoption of artificial intelligence (AI) and machine learning (ML) technologies in network traffic analysis is also a key factor driving the market's growth. These technologies enhance the capabilities of NTA software by enabling automated threat detection, predictive analytics, and anomaly detection, thereby improving the overall efficiency and accuracy of network monitoring. The integration of AI and ML has allowed NTA solutions to evolve from traditional reactive systems to proactive security platforms, capable of identifying and mitigating threats in real-time. This technological advancement is particularly attractive to large enterprises and government agencies that require robust security measures to safeguard critical infrastructure and data.

From a regional perspective, North America is anticipated to lead the NTA Software market during the forecast period, owing to the region's well-established IT infrastructure and the presence of major industry players. The Asia Pacific region, however, is expected to witness the fastest growth, driven by rapid technological advancements, increasing internet penetration, and a rising focus on cybersecurity across emerging economies such as India and China. Europe also presents significant growth opportunities, supported by stringent data protection regulations and growing investments in cybersecurity solutions. These regional dynamics highlight the diverse growth trajectories and opportunities present across the global NTA Software market.

Component Analysis

The Network Traffic Analysis Software market is segmented into two primary components: software and services. The software segment accounts for the largest share of the market and is expected to continue its dominance throughout the forecast period. This is primarily due to the increasing demand for advanced network traffic analysis solutions that can efficiently monitor, detect, and respond to potential security threats. With the escalating frequency of cyberattacks, organizations are increasingly leveraging sophisticated software to enhance their network security posture and mitigate risks. The software component includes various solutions such as real-time traffic monitoring, anomaly detection, and threat intelligence, which are integral to comprehensive network security strategies.

The services segment, on the other hand, is projected to witness signi

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

The City of Pasadena has a longstanding interest in protecting neighborhoods from cut-through traffic and speeding vehicles. As early as the 1980’s, the City authorized installation of speed humps to slow traffic in residential areas. Today, almost 400 of these traffic management devices have been installed along with many other traffic management measures.Traffic counts are conducted throughout the City of Pasadena either through resident requests, development projects, specific and general plans, or engineering studies. The Department of Transportation has collected these traffic counts and made them available to the public through the use of a Traffic Count Database.

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.

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

As many general retailers or mass distribution channels experienced an exponential growth during the months of the COVID-19 induced lockdown in France, the source wanted to measure the traffic growth of the different retailers on a yearly basis. Thus, since **********, Auchan has been doing better on a year on year basis, with ***** percent more traffic in **********. In terms of page views, Auchan was able to score *** for the average number of pages views on its website.

Each point holds the measured average daily traffic volume for that site during the most recent survey. The site type attribute defines whether a classifier was used for the traffic count.Historical count data is available on the ADT website located at https://apps.co.marion.or.us/adt/

As many general retailers or mass distribution channels experienced an exponential growth during the months of the COVID-19 induced lockdown in France, the source wanted to measure the share of direct traffic of the different retailers websites. Thus, we note that around ** percent of visits to Casino.fr came from direct traffic, that is to say, visits made through search engines, social media, blogs, or other websites that have links to other websites.

The GTT23 dataset contains network metadata of encrypted traffic measured from exit relays in the Tor network over a 13-week measurement period in 2023. The metadata is suitable for analyzing and evaluating website fingerprinting attacks and defenses.

Our dataset measurement process was designed to prioritize safety and privacy and was developed through consultation with the Tor Research Safety Board (TRSB, submission #37). Our TRSB interaction resulted in a “No Objections” score.

The measurement process, additional safety and ethical considerations, and a statistical analysis of the dataset will be presented in further detail in a forthcoming publication.

Context

Using the web analytic tool, such as Google Analytics, airline will be able to track and measure different KPIs. From the website traffic to E-commerce data

Content

The dataset is a fraction of a large booking dataset with the detail of Fare Options (1~4) visible on website, together with the the booked fare amount. Airline normally will be utilize it to understand user's upsell behavior.

The dataset is cleaned and specifically for a single outbound market (Thailand) with each Row represents one single booking record

As many general retailers or mass distribution channels experienced an exponential growth during the months of the COVID-19 induced lockdown in France, the source wanted to measure the share of organic traffic of the different retailers websites. Thus, we note that around ** percent of visits to Franprix.fr came from organic traffic, that is to say, visits coming from search engines such as Google or Bing. The “minor” competitors in the sector have clearly understood that the fight against the big names (and their direct traffic) requires an elaborate keyword strategy.

Facebook is a web traffic powerhouse: in March 2024 approximately 16.6 billion visits were measured to the Facebook.com, making it one of the most-visited websites online. In the third quarter of 2023, Facebook had nearly three billion monthly active users.

During a survey among marketers in the United States published in the second half of 2024, approximately ** percent of respondents reported using sales increase as a method of measuring programmatic digital out-of-home (prDOOH) advertising attribution. The impact on the website traffic ranked second, with a 49-percent share. According to the same study, the world's top ways of measuring prDOOH attribution included an increase in performance when planned with other digital channels and attention such as dwell time or eye-tracking data.