Code:

Packet_Features_Generator.py & Features.py

To run this code:

pkt_features.py [-h] -i TXTFILE [-x X] [-y Y] [-z Z] [-ml] [-s S] -j

-h, --help show this help message and exit -i TXTFILE input text file -x X Add first X number of total packets as features. -y Y Add first Y number of negative packets as features. -z Z Add first Z number of positive packets as features. -ml Output to text file all websites in the format of websiteNumber1,feature1,feature2,... -s S Generate samples using size s. -j

Purpose:

Turns a text file containing lists of incomeing and outgoing network packet sizes into separate website objects with associative features.

Uses Features.py to calcualte the features.

startMachineLearning.sh & machineLearning.py

To run this code:

bash startMachineLearning.sh

This code then runs machineLearning.py in a tmux session with the nessisary file paths and flags

Options (to be edited within this file):

--evaluate-only to test 5 fold cross validation accuracy

--test-scaling-normalization to test 6 different combinations of scalers and normalizers

Note: once the best combination is determined, it should be added to the data_preprocessing function in machineLearning.py for future use

--grid-search to test the best grid search hyperparameters - note: the possible hyperparameters must be added to train_model under 'if not evaluateOnly:' - once best hyperparameters are determined, add them to train_model under 'if evaluateOnly:'

Purpose:

Using the .ml file generated by Packet_Features_Generator.py & Features.py, this program trains a RandomForest Classifier on the provided data and provides results using cross validation. These results include the best scaling and normailzation options for each data set as well as the best grid search hyperparameters based on the provided ranges.

Data

Encrypted network traffic was collected on an isolated computer visiting different Wikipedia and New York Times articles, different Google search queres (collected in the form of their autocomplete results and their results page), and different actions taken on a Virtual Reality head set.

Data for this experiment was stored and analyzed in the form of a txt file for each experiment which contains:

First number is a classification number to denote what website, query, or vr action is taking place.

The remaining numbers in each line denote:

The size of a packet,

and the direction it is traveling.

negative numbers denote incoming packets

positive numbers denote outgoing packets

Figure 4 Data

This data uses specific lines from the Virtual Reality.txt file.

The action 'LongText Search' refers to a user searching for "Saint Basils Cathedral" with text in the Wander app.

The action 'ShortText Search' refers to a user searching for "Mexico" with text in the Wander app.

The .xlsx and .csv file are identical

Each file includes (from right to left):

The origional packet data,

each line of data organized from smallest to largest packet size in order to calculate the mean and standard deviation of each packet capture,

and the final Cumulative Distrubution Function (CDF) caluclation that generated the Figure 4 Graph.

The competitive landscape of the website analytics market, encompassing players like Google, BuiltWith, SEMrush, and others, is dynamic and characterized by significant growth. The market's size in 2025 is estimated at $15 billion, reflecting a Compound Annual Growth Rate (CAGR) of 15% from 2019. This robust growth is driven by increasing reliance on data-driven decision-making across businesses, expanding digital marketing strategies, and the rise of e-commerce. Key trends include the integration of AI and machine learning for more sophisticated analysis, the increasing demand for real-time data, and a growing focus on personalized user experiences. While the market faces constraints such as data privacy concerns and the complexity of integrating diverse data sources, the overall outlook remains highly positive. The market is segmented by solution type (website analytics, social media analytics, app analytics), deployment mode (cloud, on-premise), and enterprise size (small, medium, large). Companies are focusing on developing advanced analytical capabilities, strengthening partnerships, and expanding their global reach to maintain their competitive edge. The competitive analysis reveals a clear dominance by established players such as Google Analytics, leveraging its massive user base and comprehensive feature set. However, specialized tools like SEMrush and Ahrefs cater to niche needs like SEO analysis and backlink profiling. Smaller players often differentiate themselves through specialized features, superior customer support, or cost-effectiveness, carving out space within the market. Future market share will largely depend on the ability of companies to innovate, adapt to changing privacy regulations, and successfully integrate cutting-edge technologies like AI and machine learning into their offerings. The competition is expected to intensify further with the emergence of new players and the constant evolution of analytical techniques. Strategic mergers and acquisitions are also likely to reshape the market structure in the coming years.

Annual average daily traffic is the total volume for the year divided by 365 days. The traffic count year is from October 1st through September 30th. Very few locations in California are actually counted continuously. Traffic Counting is generally performed by electronic counting instruments moved from location throughout the State in a program of continuous traffic count sampling. The resulting counts are adjusted to an estimate of annual average daily traffic by compensating for seasonal influence, weekly variation and other variables which may be present. Annual ADT is necessary for presenting a statewide picture of traffic flow, evaluating traffic trends, computing accident rates. planning and designing highways and other purposes.Traffic Census Program Page

The global website analytics market, encompassing solutions for large enterprises and SMEs, is poised for significant growth. While the provided data lacks specific market size and CAGR figures, a reasonable estimation based on industry trends suggests a 2025 market size of approximately $15 billion, experiencing a compound annual growth rate (CAGR) of 12% from 2025 to 2033. This robust growth is fueled by several key drivers: the increasing reliance on data-driven decision-making across businesses, the escalating need for enhanced website performance optimization, and the growing adoption of sophisticated analytics tools offering deeper insights into user behavior and conversion rates. Market segmentation reveals strong demand across diverse analytics types, including product, traffic, and sales analytics. The competitive landscape is intensely dynamic, with established players like Google, SEMrush, and SimilarWeb vying for market share alongside emerging innovative companies like Owletter and TrendSource. These companies are constantly innovating to provide more comprehensive and user-friendly analytics platforms, leading to increased competition. This competitive pressure fosters innovation, but also necessitates strategic differentiation, focusing on specific niche markets or offering unique features to attract and retain customers. The market’s geographic distribution shows significant traction in North America and Europe, but emerging markets in Asia Pacific are also exhibiting substantial growth potential, driven by increasing internet penetration and digital transformation initiatives. While data security concerns and the complexity of implementing analytics tools present some restraints, the overall market outlook remains highly positive, promising considerable opportunities for market participants in the coming years.

In 2022, about ***** out of ten representatives of business companies and advertising agencies in Russia measured content marketing effectiveness by analyzing digital metrics. Furthermore, ** percent of the survey participants tracked website traffic and identified the source of the content. Nearly ** percent did not evaluate content marketing efficiency.

This dataset contains anonymized layer 1-4 packet headers of two-way passive traces captured on a 100 GB link between Los Angeles and San Jose. These data are useful for research on the characteristics of Internet traffic, including application breakdown, security events, geographic and topological distribution, flow volume and duration.

Passive 100G sampler is offered to researchers at commercial organizations when they request Anonymized Internet Traces. These data are part of the 2024 Anonymized Traces 100G dataset. The files consist of 5 second snapshots of a bidirectional capture taken in November 2024.

Scientists are increasingly engaging the web to provide formal and informal science education opportunities. Despite the prolific growth of web-based resources, systematic evaluation and assessment of their efficacy remains limited. We used clickstream analytics, a widely available method for tracking website visitors and their behavior, to evaluate >60,000 visits over three years to an educational website focused on ecology. Visits originating from search engine queries were a small proportion of the traffic, suggesting the need to actively promote websites to drive visitation. However, the number of visits referred to the website per social media post varied depending on the social media platform and the quality of those visits (e.g., time on site and number of pages viewed) was significantly lower than visits originating from other referring websites. In particular, visitors referred to the website through targeted promotion (e.g., inclusion in a website listing classroom teaching...

The most common indicator to consider while assessing the effectiveness of online brand advertisements in Russia in 2021 was the website traffic, as per ** percent of surveyed company representatives. Furthermore, approximately ********** measured it by the frequency of search queries related to their brands. About ** percent of participants stated their enterprises evaluated the success of performance ads by checking the number of clicks on the website.

Annual average daily traffic is the total volume for the year divided by 365 days. The truck count year is from October 1st through September 30th. Very few locations in California are actually counted continuously. Truck Counting is generally performed by electronic counting instruments moved from location throughout the State in a program of continuous traffic count sampling. The resulting counts are adjusted to an estimate of annual average daily traffic by compensating for seasonal influence, weekly variation and other variables which may be present. Annual ADT is necessary for presenting a statewide picture of traffic flow, evaluating traffic trends, computing accident rates. planning and designing highways and other purposes.

In June 2020, the decision was taken to implement a low traffic neighbourhood trial in The Groves. For more information on the trial please visit City of York Council's website Independent monitoring and evaluation work has been commissioned by CYC to assess the impact of the trial and inform future decisions on the experimental road closures in The Groves. Part of this work uses traffic surveys which are available in this dataset which includes baseline surveys for: • the week before the start of the trial (week 1) • and the first two weeks of the trial (weeks 2 and 3). • Approx. A year after the start of the trial (included in The Groves Traffic Analysis). • Bus journey time data before and during the trial (The Groves Bus Analysis)

Dataset 1 (AXA collisions 2015–2019) was curated and used to evaluate the effect of two road traffic regulations implemented in Mexico City in 2015 and 2019 on collisions using an interrupted time series analysis. Collisions data came from insurance collision claims (January 2015 to December 2019). The dataset contains 8 variables: year (anio_n), week (semana), count of total collisions per week (c_total), count of collisions resulting in injury per week (c_p_lesion), binary variable to identify the 2015 intervention (limit), binary variable to identify the 2019 intervention (limit1), the number of weeks from baseline (time), an estimate of the number of insured vehicles per week (veh_a_cdmx). Dataset 2 (Road traffic deaths 2013–2019) was curated and used to evaluate the effect of two road traffic regulations implemented in Mexico City in 2015 and 2019 on mortality using an interrupted time series analysis. Mortality data came from vital registries collated by the Mexican Institute for Geography and Statistics, INEGI, (January 2013 to December 2019). The dataset contains 7 variables: year (anio_ocur), week (semana), count of traffic-related deaths per week (def_trans), binary variable to identify the 2015 intervention (limit), binary variable to identify the 2019 intervention (limit1), the number of weeks from baseline (time) and an estimate of the Mexico City population per week (pob_tot_p). Methods Dataset 1 arises from publicly available data on insurance-reported collisions published on the website of the International Institute for Data Science (see reference below). The data were collected by claims adjusters from the company AXA at the site of the collision using an electronic device. These data were available for public use from January 2015 to December 2019 and include information on individual collisions and their characteristics: date the collision occurred, location (coordinates and adjuster reported location), type of vehicle involved and whether there were injuries or deaths. Data were processed and cleaned, mapping collisions, and keeping only those georeferenced within Mexico City boundaries as well as coded to Mexico City in the reported location variable. We then summed the number of collisions per week and merged it with data on an estimate of the number of insured registered vehicles per week (using information from registered vehicles and proportion of insured vehicles from the Mexican Association of Insurance companies). Two more variables were created, one that identifies the week when the intervention came into effect and another variable to number the weeks since baseline. This dataset contains all the necessary information to conduct the interrupted time series analysis for total collisions and collisions resulting in injuries. Dataset 2: mortality data were validated and reported by INEGI (see reference below) from death certificates filed mainly by the Health Sector, using the International Classification of Disease, 10th Revision (ICD-10) for diagnosis codes. We used data from January 2013 to December 2019 and included deaths with the following ICD-10 codes: V02-V04 (.1-.9), V09, V092, V09.3, V09.9, V12-V14 (.3-.9), V19.4-V19.6, V19.9, V20-V28 (.3-.9), V29, V30-V39, V40-V79 (.4-.9), V80.3-V80.5, V81.1, V82.1, V82.1, V83-V86 (.0-.3), V87-V89.2 and V89.9. We summed the number of traffic-related deaths per week and merged it with data on an estimate of the total population in Mexico City per week (see refs below). Two more variables were created, one that identifies the week when the intervention came into effect and another variable to number the weeks since baseline. This dataset contains all the necessary information to conduct the interrupted time series analysis for road traffic deaths. References to original data:

Instituto Internacional de Ciencia de Datos. Datos AXA de Percances Viales [Internet]. 2020 [July 2021]. Available from: https://i2ds.org/datos-abiertos/. Instituto Nacional de Geografía y Estadística. Parque Vehicular [Internet]. 2019 [July 2021]. Available from: https://www.inegi.org.mx/temas/vehiculos/default.html#Tabulados. Dirección Ejecutiva de Líneas de Negocio área de Automóviles. Sistema Estadístico del Sector Asegurador del ramo Automóviles SESA 2018. Mexico City: Asociación Mexicana de Instituciones de Seguro, 2020. Instituto Nacional de Geografía y Estadística. Mortalidad [Internet]. 2020 [July 2021]. Available from: https://www.inegi.org.mx/programas/mortalidad/default.html#Datos_abiertos.

World Health Organisation. ICD-10 Version:2010 [Internet]. 2010 [July 2021]. Available from: https://icd.who.int/browse10/2010/en. Consejo Nacional de Población. Proyecciones de la Población de México y de las Entidades Federativas, 2016-2050 [Internet]. 2018 [July 2021]. Available from: https://datos.gob.mx/busca/dataset/proyecciones-de-la-poblacion-de-mexico-y-de-las-entidades-federativas-2016-2050.

SEO Testing Service Market Outlook

The global SEO Testing Service market size was valued at approximately USD 1.5 billion in 2023 and is projected to reach USD 4.3 billion by 2032, growing at a CAGR of 12.5% during the forecast period. The growth of this market can be attributed to the increasing importance of digital marketing and the need for businesses to optimize their online presence to stay competitive.

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