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.

This repository is part of the ITC-NetMingledApp dataset, which includes network traffic data from 36 Android applications, with each capture featuring concurrent traffic from multiple applications and smartphones. This repository contains data related to the Iran-Qom scenario. Each capture is stored in a compressed file containing the relevant PCAP files of the associated applications. The PCAP files are named according to a convention: {TimeStamp}_{Application Name}{Download-Upload Speed}.pcap

The global number of smartphone users in was forecast to continuously increase between 2024 and 2029 by in total 1.8 billion users (+42.62 percent). After the ninth consecutive increasing year, the smartphone user base is estimated to reach 6.1 billion users and therefore a new peak in 2029. Notably, the number of smartphone users of was continuously increasing over the past years.Smartphone users here are limited to internet users of any age using a smartphone. The shown figures have been derived from survey data that has been processed to estimate missing demographics.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the number of smartphone users in countries like Australia & Oceania and Asia.

This repository is part of the ITC-NetMingledApp dataset, which includes network traffic data from 36 Android applications, with each capture featuring concurrent traffic from multiple applications and smartphones. This repository contains part #2 of the data related to the Iran-Tehran scenario. Each capture is stored in a compressed file containing the relevant PCAP files of the associated applications. The PCAP files are named according to a convention: {TimeStamp}_{Application Name}{Download-Upload Speed}.pcap Part #1 of Iran-Tehran scenario is in the Tehran Dataset #1 (https://doi.org/10.17632/9frgkybxhn.1) repository.

Introduction

Android is one of the most used mobile operating systems worldwide. Due to its technological impact, its open-source code and the possibility of installing applications from third parties without any central control, Android has recently become a malware target. Even if it includes security mechanisms, the last news about malicious activities and Android´s vulnerabilities point to the importance of continuing the development of methods and frameworks to improve its security.

To prevent malware attacks, researches and developers have proposed different security solutions, applying static analysis, dynamic analysis, and artificial intelligence. Indeed, data science has become a promising area in cybersecurity, since analytical models based on data allow for the discovery of insights that can help to predict malicious activities.

In this work, we propose to consider some network layer features as the basis for machine learning models that can successfully detect malware applications, using open datasets from the research community.

Content

This dataset is based on another dataset (DroidCollector) where you can get all the network traffic in pcap files, in our research we preprocessed the files in order to get network features that are illustrated in the next article:

López, C. C. U., Villarreal, J. S. D., Belalcazar, A. F. P., Cadavid, A. N., & Cely, J. G. D. (2018, May). Features to Detect Android Malware. In 2018 IEEE Colombian Conference on Communications and Computing (COLCOM) (pp. 1-6). IEEE.

Acknowledgements

Cao, D., Wang, S., Li, Q., Cheny, Z., Yan, Q., Peng, L., & Yang, B. (2016, August). DroidCollector: A High Performance Framework for High Quality Android Traffic Collection. In Trustcom/BigDataSE/I SPA, 2016 IEEE (pp. 1753-1758). IEEE

The population share with mobile internet access in North America was forecast to increase between 2024 and 2029 by in total 2.9 percentage points. This overall increase does not happen continuously, notably not in 2028 and 2029. The mobile internet penetration is estimated to amount to 84.21 percent in 2029. Notably, the population share with mobile internet access of was continuously increasing over the past years.The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection.The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).Find more key insights for the population share with mobile internet access in countries like Caribbean and Europe.

Unlock the Power of Behavioural Data with GDPR-Compliant Clickstream Insights.

Swash clickstream data offers a comprehensive and GDPR-compliant dataset sourced from users worldwide, encompassing both desktop and mobile browsing behaviour. Here's an in-depth look at what sets us apart and how our data can benefit your organisation.

User-Centric Approach: Unlike traditional data collection methods, we take a user-centric approach by rewarding users for the data they willingly provide. This unique methodology ensures transparent data collection practices, encourages user participation, and establishes trust between data providers and consumers.

Wide Coverage and Varied Categories: Our clickstream data covers diverse categories, including search, shopping, and URL visits. Whether you are interested in understanding user preferences in e-commerce, analysing search behaviour across different industries, or tracking website visits, our data provides a rich and multi-dimensional view of user activities.

GDPR Compliance and Privacy: We prioritise data privacy and strictly adhere to GDPR guidelines. Our data collection methods are fully compliant, ensuring the protection of user identities and personal information. You can confidently leverage our clickstream data without compromising privacy or facing regulatory challenges.

Market Intelligence and Consumer Behaviuor: Gain deep insights into market intelligence and consumer behaviour using our clickstream data. Understand trends, preferences, and user behaviour patterns by analysing the comprehensive user-level, time-stamped raw or processed data feed. Uncover valuable information about user journeys, search funnels, and paths to purchase to enhance your marketing strategies and drive business growth.

High-Frequency Updates and Consistency: We provide high-frequency updates and consistent user participation, offering both historical data and ongoing daily delivery. This ensures you have access to up-to-date insights and a continuous data feed for comprehensive analysis. Our reliable and consistent data empowers you to make accurate and timely decisions.

Custom Reporting and Analysis: We understand that every organisation has unique requirements. That's why we offer customisable reporting options, allowing you to tailor the analysis and reporting of clickstream data to your specific needs. Whether you need detailed metrics, visualisations, or in-depth analytics, we provide the flexibility to meet your reporting requirements.

Data Quality and Credibility: We take data quality seriously. Our data sourcing practices are designed to ensure responsible and reliable data collection. We implement rigorous data cleaning, validation, and verification processes, guaranteeing the accuracy and reliability of our clickstream data. You can confidently rely on our data to drive your decision-making processes.

Switzerland is leading the ranking by population share with mobile internet access, recording 95.06 percent. Following closely behind is Ukraine with 95.06 percent, while Moldova is trailing the ranking with 46.83 percent, resulting in a difference of 48.23 percentage points to the ranking leader, Switzerland. The penetration rate refers to the share of the total population having access to the internet via a mobile broadband connection. The shown data are an excerpt of Statista's Key Market Indicators (KMI). The KMI are a collection of primary and secondary indicators on the macro-economic, demographic and technological environment in up to 150 countries and regions worldwide. All indicators are sourced from international and national statistical offices, trade associations and the trade press and they are processed to generate comparable data sets (see supplementary notes under details for more information).

As technology advances, the number and complexity of cyber-attacks increase, forcing defense techniques to be updated and improved. To help develop effective tools for detecting security threats it is essential to have reliable and representative security datasets. Many existing security datasets have limitations that make them unsuitable for research, including lack of labels, unbalanced traffic, and outdated threats.

CTU-SME-11 is a labeled network dataset designed to address the limitations of previous datasets. The dataset was captured in a real network that mimics a small-medium enterprise setting. Raw network traffic (packets) was captured from 11 devices using tcpdump for a duration of 7 days, from 20th to 26th of February, 2023 in Prague, Czech Republic. The devices were chosen based on the enterprise setting and consists of IoT, desktop and mobile devices, both bare metal and virtualized. The devices were infected with malware or exposed to Internet attacks, and factory reset to restore benign behavior.

The raw data was processed to generate network flows (Zeek logs) which were analyzed and labeled. The dataset contains two types of levels, a high level label and a descriptive label, which were put by experts. The former can take three values, benign, malicious or background. The latter contains detailed information about the specific behavior observed in the network flows. The dataset contains 99 million labeled network flows. The overall compressed size of the dataset is 80GB and the uncompressed size is 170GB.

This dataset includes network traffic data from more than 50 Android applications across 5 different scenarios. The applications are consistent in all scenarios, but other factors like location, device, and user vary (see Table 2 in the paper). The current repository pertains to Scenario D. Within the repository, for each application, there is a compressed file containing the relevant PCAP files. The PCAP files follow the naming convention: {Application Name}{Scenario ID}{#Trace}_Final.pcap.

Dataset description

The dataset description will start with describing the local conditions and other metadata, then will continue with describing the recording procedure and annotation methodology. Finally, a brief description of the dataset deployment and publication will be given.

Meta Information

The dataset was recorded at King's College London (KCL) Hospital, Denmark Hill, Brixton, London SE5 9RS in the period from 26 to 29 September 2017. We used a typical examination room with about ten square meters area and a typical reverberation tome of approx. 500ms to perform the voice recordings. Due to the fact, that the voice recordings are performed in the realistic situation of doing a phone call (i.e. participant holds the phone to the preferred ear and microphone is in direct proximity to the mouth), one can assume that all recordings were performed within the reverberation radius and thus can be considered as “clean”.

Recording Procedure

We used a Motorola Moto G4 Smartphone as recording device. To perform the voice recordings on the device, we developed a “Toggle Recording App”, which uses the same functionalities as the voice recording module used within the i-PROGNOSIS Smartphone application, but deployed as a standalone android application. This means, that the voice capturing service runs as a standalone background service on the recording device and triggers voice recordings via on- and off-hook signals of the Smartphone. Due to the fact, that we directly record the microphone signal, and not the GSM (“Global System for Mobile Communications”) compressed stream, we end up with high quality recordings with a sample rate of 44.1 kHz and a bit depth of 16 Bit (audio CD quality). The raw, uncompressed data is directly written to the external storage of the Smartphone (SD-card) using the well-known WAVE file format (.wav). We used the following workflow to perform a voice recording:

Ask the participant to relax a bit and then to make a phone call to the test executor (off-hook signal triggered).}

Ask the participant to read out “The North Wind and the Sun”

Depending on the constitution of the participant either ask to read out “Tech. Engin. Computer applications in geography snippet”

Start a spontaneous dialog with the participant, the test executor starts asking random questions about places of interest, local traffic, or personal interests if acceptable.

Test executor ends call by farewell (on-hook signal triggered).

Annotation Scheme

For each HC and PD participant, we labeled the data regarding scores on the Hoehn & Yahr (H&Y), as well as the UPDRS II part 5 and UPDRS III part 18 scale. The voice recordings are labeled in the following scheme:

SI_ HS_ HYR_ UPDRS II-5_UPDRS III-18

with

SI as subject identification in the form IDNN, N in [0, 9]

HS as the health status label (hc or pd accordingly)

HYR as the expert assessed H&Y scale rating

UPDRS II-5 as the according expert peer-reviewed score

UPDRS III-18 as the according expert assessed score

For example, an audio recording with the file name “ID02_pd_1_2_1.wav” represents a recording of the third participant (First participant was anonymized as ID00), which has PD and a H&Y rating of 1, a UPDRS II-5 score of 2 and a UPDRS III-18 score of 1. At this point, it should be noted, that also all healthy controls were evaluated with regard to the introduced scales, because Parkinson's disease and voice degradation correlate, but don't match exactly. This means, that the data set includes one HC participant (ID31) with UPDRS II-5 and III-18 rating of 1, and also includes PD patients with UPDRS II-5 and III-18 ratings of 0. It should be emphasized, that this does not mean the data set includes ambiguous information, but that an expert was not able to hear voice degradation that would end up in a UPDRS rating greater than zero. Machine learning approaches may be able to nevertheless classify correctly, or at least learn to correlate, but not match PD and voice degradation at any time.

Appendix

North Wind and the Sun (Orthographic Version):

“The North Wind and the Sun were disputing which was the stronger, when a traveler came along wrapped in a warm cloak. They agreed that the one who first succeeded in making the traveler take his cloak off should be considered stronger than the other. Then the North Wind blew as hard as he could, but the more he blew the more closely did the traveler fold his cloak around him; and at last the North Wind gave up the attempt. Then the Sun shone out warmly, and immediately the traveler took off his cloak. And so the North Wind was obliged to confess that the Sun was the stronger of the two.”

BNC – Tech. Engin. Computer applications in geography snippet:

“[...] This is because there is less scattering of blue light as the atmospheric path length and consequently the degree of scattering of the incoming radiation is reduced. For the same reason, the sun appears to be whiter and less orange-coloured as the observer's altitude increases; this is because a greater proportion of the sunlight comes directly to the observer's eye. Figure 5.7 is a schematic representation of the path of electromagnetic energy in the visible spectrum as it travels from the sun to the Earth and back again towards a sensor mounted on an orbiting satellite. The paths of waves representing energy prone to scattering (that is, the shorter wavelengths) as it travels from sun to Earth are shown. To the sensor it appears that all the energy has been reflected from point P on the ground whereas, in fact, it has not, because some has been scattered within the atmosphere and has never reached the ground at all. [...]”

Datos brings to market anonymized, at scale, consolidated privacy-secured datasets with a granularity rarely found in the market. Get access to the desktop and mobile browsing behavior for millions of users across the globe, packaged into clean, easy-to-understand data products and reports.

The Datos Activity Feed is an event-level accounting of all observed URL visits executed by devices which Datos has access to over a given period of time.

This feed can be delivered on a daily basis, delivering the previous day’s data. It can be filtered by any of the fields, so you can focus on what’s important for you, whether it be specific markets or domains.

Now available with Datos Low-Latency Feed This add-on ensures delivery of approximately 99% of all devices before markets open in New York (the lowest latency product on the market). Our clickstream data is made up of an array of upstream sources. The DLLF makes the daily output of these sources available as they arrive and are processed, rather than a once-daily batch.

This dataset contains 30,925 labelled and cleaned records collected from a dense 2 km² urban area surrounding Sunway University, Selangor, Malaysia. Using the GNetTrack Pro mobile application and Samsung S21 Ultra devices, the data spans three anonymized commercial mobile network operators and includes both 4G and 5G technologies. The dataset captures radio signal quality metrics (RSRP, RSRQ, SNR, etc.), geospatial information, mobility patterns (walking vs. driving), and application-specific traffic scenarios (HTTP, FTP, 1080p Video Streaming).

A total of 132 physical cell sites were validated via OpenCellID and field inspections. The dataset is released in CSV format and includes Python scripts for data preprocessing and basic visualization. This makes it a valuable resource for machine learning tasks like signal metric regression, handover optimization, and QoE modeling in heterogeneous and simulation of high-density urban networks.

Key features:

Real-world 5G/4G measurements

Multi-operator and multi-mobility modes

Traffic-aware profiling

Empirical validation of base station locations

Ready for ML/DL use cases

For more information look out for our article about the dataset on data in brief journal.

The Views of ABC News Digital Content dataset provides both the number of page/screen views per hour for individual pieces of ABC News content and metadata related to each piece of content. The data is taken from across different ABC digital platforms during the month of May 2016. These platforms include the ABC News desktop and mobile websites and the ABC app (both iOS and Android versions). Each piece of content is represented by its ID and is consistent for the same piece of content across platforms. The URL of the content can be recreated using the platform and this ID. For example, for the “News” platform and id “7373616”, the URL is retrieved using “http://www.abc.net.au/news/7373616”. The content ID is the key which joins the Traffic data with the Content Metadata. The data set covers the period from 2016-05-01 00:00:00 to 2016-05-31 23:59:59.

Rights information

This data includes metadata about existing publicly available stories. In general terms, developers are free to use this data to explore ABC's content. But original stories and images should always be linked to. Stories and photos should not be reproduced in whole on another service. _ The stories themselves and their associated media items still remain the property of ABC and other rights holders where noted._ Full details of copyright and more are listed on abc.net.au: http://www.abc.net.au/conditions.htm

Datos brings to market anonymized, at scale, consolidated privacy-secured datasets with a granularity rarely found in the market. Get access to the desktop and mobile browsing behavior for millions of users across the globe, packaged into clean, easy-to-understand data products and reports.

The Datos Domain Traffic Feed reports on panelist visitation to domains, benchmarking the popularity of internet properties worldwide by country. Additionally, we offer the ability to track the availability of domains with respect to whether traffic is being sent to sites which are currently unregistered. Customers can elect to focus on specific domains, countries, or domain registration status.

Now available with Datos Low-Latency Feed This add-on ensures delivery of approximately 99% of all devices before markets open in New York (the lowest latency product on the market). Our clickstream data is made up of an array of upstream sources. The DLLF makes the daily output of these sources available as they arrive and are processed, rather than a once-daily batch.

We present a dataset targeting a large set of popular pages (Alexa top-500), from probes from several ISPs networks, browsers software (Chrome, Firefox) and viewport combinations, for over 200,000 experiments realized in 2019.We purposely collect two distinct sets with two different tools, namely Web Page Test (WPT) and Web View (WV), varying a number of relevant parameters and conditions, for a total of 200K+ web sessions, roughly equally split among WV and WPT. Our dataset comprises variations in terms of geographical coverage, scale, diversity and representativeness (location, targets, protocol, browser, viewports, metrics).For Web Page Test, we used the online service www.webpagetest.org at different locations worldwide (Europe, Asia, USA) and private WPT instances in three locations in China (Beijing, Shanghai, Dongguan). The list of target URLs comprised the main pages and five random subpages from Alexa top-500 worldwide and China. We varied network conditions : native connections and 4G, FIOS, 3GFast, DSL, and custom shaping/loss conditions. The other elements in the configuration were fixed: Chrome browser on desktop with a fixed screen resolution, HTTP/2 protocol and IPv4.For Web View, we collected experiments from three machines located in France. We selected two versions of two browser families (Chrome 75/77, Firefox 63/68), two screen sizes (1920x1080, 1440x900), and employ different browser configurations (one half of the experiments activate the AdBlock plugin) from two different access technologies (fiber and ADSL). From a protocol standpoint, we used both IPv4 and IPv6, with HTTP/2 and QUIC, and performed repeated experiments with cached objects/DNS. Given the settings diversity, we restricted the number of websites to about 50 among the Alexa top-500 websites, to ensure statistical relevance of the collected samples for each page.The two archives IFIPNetworking2020_WebViewOrange.zip and IFIPNetworking2020_Webpagetest.zip correspond respectively to the Web View experiments and to the Web Page Test experiments.Each archive contains three files:- config.csv: Description of parameters and conditions for each run,- metrics.csv: Value of different metrics collected by the browser,- progressionCurves.csv: Progression curves of the bytes progress as seen by the network, from 0 to 10 seconds by steps of 100 milliseconds,- listUrl folder: Indexes the sets of urls.Regarding config.csv, the columns are: - index: Index for this set of conditions, - location: Location of the machine, - listUrl: List of urls, located in the folder listUrl - browserUsed: Internet browser and version - terminal: Desktop or Mobile - collectionEnvironment: Identification of the collection environment - networkConditionsTrafficShaping (WPT only): Whether native condition or traffic shaping (4G, FIOS, 3GFast, DSL, or custom Emulator conditions) - networkConditionsBandwidth (WPT only): Bandwidth of the network - networkConditionsDelay (WPT only): Delay in the network - networkConditions (WV only): network conditions - ipMode (WV only): requested L3 protocol, - requestedProtocol (WV only): requested L7 protocol - adBlocker (WV only): Whether adBlocker is used or not - winSize (WV only): Window sizeRegarding metrics.csv, the columns are: - id: Unique identification of an experiment (consisting of an index 'set of conditions' and an index 'current page') - DOM Content Loaded Event End (ms): DOM time, - First Paint (ms) (WV only): First paint time, - Load Event End (ms): Page Load Time from W3C, - RUM Speed Index (ms) (WV only): RUM Speed Index, - Speed Index (ms) (WPT only): Speed Index, - Time for Full Visual Rendering (ms) (WV only): Time for Full Visual Rendering - Visible portion (%) (WV only): Visible portion, - Time to First Byte (ms) (WPT only): Time to First Byte, - Visually Complete (ms) (WPT only): Visually Complete used to compute the Speed Index, - aatf: aatf using ATF-chrome-plugin - bi_aatf: bi_aatf using ATF-chrome-plugin - bi_plt: bi_plt using ATF-chrome-plugin - dom: dom using ATF-chrome-plugin - ii_aatf: ii_aatf using ATF-chrome-plugin - ii_plt: ii_plt using ATF-chrome-plugin - last_css: last_css using ATF-chrome-plugin - last_img: last_img using ATF-chrome-plugin - last_js: last_js using ATF-chrome-plugin - nb_ress_css: nb_ress_css using ATF-chrome-plugin - nb_ress_img: nb_ress_img using ATF-chrome-plugin - nb_ress_js: nb_ress_js using ATF-chrome-plugin - num_origins: num_origins using ATF-chrome-plugin - num_ressources: num_ressources using ATF-chrome-plugin - oi_aatf: oi_aatf using ATF-chrome-plugin - oi_plt: oi_plt using ATF-chrome-plugin - plt: plt using ATF-chrome-pluginRegarding progressionCurves.csv, the columns are: - id: Unique identification of an experiment (consisting of an index 'set of conditions' and an index 'current page') - url: Url of the current page. SUBPAGE stands for a path. - run: Current run (linked with index of the config for WPT) - filename: Filename of the pcap - fullname: Fullname of the pcap - har_size: Size of the HAR for this experiment, - pagedata_size: Size of the page data report - pcap_size: Size of the pcap - App Byte Index (ms): Application Byte Index as computed from the har file (in the browser) - bytesIn_APP: Total bytes in as seen in the browser, - bytesIn_NET: Total bytes in as seen in the network, - X_BI_net: Network Byte Index computed from the pcap file (in the network) - X_bin_0_for_B_completion to X_bin_99_for_B_completion: X_bin_k_for_B_completion is the bytes progress reached after k*100 millisecondsIf you use these datasets in your research, you can reference to the appropriate paper:@inproceedings{qoeNetworking2020, title={Revealing QoE of Web Users from Encrypted Network Traffic}, author={Huet, Alexis and Saverimoutou, Antoine and Ben Houidi, Zied and Shi, Hao and Cai, Shengming and Xu, Jinchun and Mathieu, Bertrand and Rossi, Dario}, booktitle={2020 IFIP Networking Conference (IFIP Networking)}, year={2020}, organization={IEEE}}

In the fourth quarter of 2024, TikTok generated around 186 million downloads from users worldwide. Initially launched in China first by ByteDance as Douyin, the short-video format was popularized by TikTok and took over the global social media environment in 2020. In the first quarter of 2020, TikTok downloads peaked at over 313.5 million worldwide, up by 62.3 percent compared to the first quarter of 2019. TikTok interactions: is there a magic formula for content success? In 2024, TikTok registered an engagement rate of approximately 4.64 percent on video content hosted on its platform. During the same examined year, the social video app recorded over 1,100 interactions on average. These interactions were primarily composed of likes, while only recording less than 20 comments per piece of content on average in 2024. The platform has been actively monitoring the issue of fake interactions, as it removed around 236 million fake likes during the first quarter of 2024. Though there is no secret formula to get the maximum of these metrics, recommended video length can possibly contribute to the success of content on TikTok. It was recommended that tiny TikTok accounts with up to 500 followers post videos that are around 2.6 minutes long as of the first quarter of 2024. While, the ideal video duration for huge TikTok accounts with over 50,000 followers was 7.28 minutes. The average length of TikTok videos posted by the creators in 2024 was around 43 seconds. What’s trending on TikTok Shop? Since its launch in September 2023, TikTok Shop has become one of the most popular online shopping platforms, offering consumers a wide variety of products. In 2023, TikTok shops featuring beauty and personal care items sold over 370 million products worldwide. TikTok shops featuring womenswear and underwear, as well as food and beverages, followed with 285 and 138 million products sold, respectively. Similarly, in the United States market, health and beauty products were the most-selling items, accounting for 85 percent of sales made via the TikTok Shop feature during the first month of its launch. In 2023, Indonesia was the market with the largest number of TikTok Shops, hosting over 20 percent of all TikTok Shops. Thailand and Vietnam followed with 18.29 and 17.54 percent of the total shops listed on the famous short video platform, respectively.