This statistic presents the consumption frequency of desserts and toppings (not cakes and gateaux) in France from 2023. In 2023, an estimated 19 million people in France were heavy users of desserts and toppings.

As of the early of 2020, around 65.3 percent of the surveyed respondents in China perceived mobile apps that ask to access to phone call records as a privacy violation. Other risky mobile app user permissions included accesses to contacts and messages.

Login Data Set for Risk-Based Authentication

Synthesized login feature data of >33M login attempts and >3.3M users on a large-scale online service in Norway. Original data collected between February 2020 and February 2021.

This data sets aims to foster research and development for Risk-Based Authentication (RBA) systems. The data was synthesized from the real-world login behavior of more than 3.3M users at a large-scale single sign-on (SSO) online service in Norway.

The users used this SSO to access sensitive data provided by the online service, e.g., a cloud storage and billing information. We used this data set to study how the Freeman et al. (2016) RBA model behaves on a large-scale online service in the real world (see Publication). The synthesized data set can reproduce these results made on the original data set (see Study Reproduction). Beyond that, you can use this data set to evaluate and improve RBA algorithms under real-world conditions.

WARNING: The feature values are plausible, but still totally artificial. Therefore, you should NOT use this data set in productive systems, e.g., intrusion detection systems.

Overview

The data set contains the following features related to each login attempt on the SSO:

    Feature
    Data Type
    Description
    Range or Example




    IP Address
    String
    IP address belonging to the login attempt
    0.0.0.0 - 255.255.255.255


    Country
    String
    Country derived from the IP address
    US


    Region
    String
    Region derived from the IP address
    New York


    City
    String
    City derived from the IP address
    Rochester


    ASN
    Integer
    Autonomous system number derived from the IP address
    0 - 600000


    User Agent String
    String
    User agent string submitted by the client
    Mozilla/5.0 (Windows NT 10.0; Win64; ...


    OS Name and Version
    String
    Operating system name and version derived from the user agent string
    Windows 10


    Browser Name and Version
    String
    Browser name and version derived from the user agent string
    Chrome 70.0.3538


    Device Type
    String
    Device type derived from the user agent string
    (mobile, desktop, tablet, bot, unknown)1


    User ID
    Integer
    Idenfication number related to the affected user account
    [Random pseudonym]


    Login Timestamp
    Integer
    Timestamp related to the login attempt
    [64 Bit timestamp]


    Round-Trip Time (RTT) [ms]
    Integer
    Server-side measured latency between client and server
    1 - 8600000


    Login Successful
    Boolean
    True: Login was successful, False: Login failed
    (true, false)


    Is Attack IP
    Boolean
    IP address was found in known attacker data set
    (true, false)


    Is Account Takeover
    Boolean
    Login attempt was identified as account takeover by incident response team of the online service
    (true, false)

Data Creation

As the data set targets RBA systems, especially the Freeman et al. (2016) model, the statistical feature probabilities between all users, globally and locally, are identical for the categorical data. All the other data was randomly generated while maintaining logical relations and timely order between the features.

The timestamps, however, are not identical and contain randomness. The feature values related to IP address and user agent string were randomly generated by publicly available data, so they were very likely not present in the real data set. The RTTs resemble real values but were randomly assigned among users per geolocation. Therefore, the RTT entries were probably in other positions in the original data set.

The country was randomly assigned per unique feature value. Based on that, we randomly assigned an ASN related to the country, and generated the IP addresses for this ASN. The cities and regions were derived from the generated IP addresses for privacy reasons and do not reflect the real logical relations from the original data set.

The device types are identical to the real data set. Based on that, we randomly assigned the OS, and based on the OS the browser information. From this information, we randomly generated the user agent string. Therefore, all the logical relations regarding the user agent are identical as in the real data set.

The RTT was randomly drawn from the login success status and synthesized geolocation data. We did this to ensure that the RTTs are realistic ones.

Regarding the Data Values

Due to unresolvable conflicts during the data creation, we had to assign some unrealistic IP addresses and ASNs that are not present in the real world. Nevertheless, these do not have any effects on the risk scores generated by the Freeman et al. (2016) model.

You can recognize them by the following values:

ASNs with values >= 500.000

IP addresses in the range 10.0.0.0 - 10.255.255.255 (10.0.0.0/8 CIDR range)

Study Reproduction

Based on our evaluation, this data set can reproduce our study results regarding the RBA behavior of an RBA model using the IP address (IP address, country, and ASN) and user agent string (Full string, OS name and version, browser name and version, device type) as features.

The calculated RTT significances for countries and regions inside Norway are not identical using this data set, but have similar tendencies. The same is true for the Median RTTs per country. This is due to the fact that the available number of entries per country, region, and city changed with the data creation procedure. However, the RTTs still reflect the real-world distributions of different geolocations by city.

See RESULTS.md for more details.

Ethics

By using the SSO service, the users agreed in the data collection and evaluation for research purposes. For study reproduction and fostering RBA research, we agreed with the data owner to create a synthesized data set that does not allow re-identification of customers.

The synthesized data set does not contain any sensitive data values, as the IP addresses, browser identifiers, login timestamps, and RTTs were randomly generated and assigned.

Publication

You can find more details on our conducted study in the following journal article:

Pump Up Password Security! Evaluating and Enhancing Risk-Based Authentication on a Real-World Large-Scale Online Service (2022) Stephan Wiefling, Paul René Jørgensen, Sigurd Thunem, and Luigi Lo Iacono. ACM Transactions on Privacy and Security

Bibtex

@article{Wiefling_Pump_2022, author = {Wiefling, Stephan and Jørgensen, Paul René and Thunem, Sigurd and Lo Iacono, Luigi}, title = {Pump {Up} {Password} {Security}! {Evaluating} and {Enhancing} {Risk}-{Based} {Authentication} on a {Real}-{World} {Large}-{Scale} {Online} {Service}}, journal = {{ACM} {Transactions} on {Privacy} and {Security}}, doi = {10.1145/3546069}, publisher = {ACM}, year = {2022} }

License

This data set and the contents of this repository are licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license. See the LICENSE file for details. If the data set is used within a publication, the following journal article has to be cited as the source of the data set:

Stephan Wiefling, Paul René Jørgensen, Sigurd Thunem, and Luigi Lo Iacono: Pump Up Password Security! Evaluating and Enhancing Risk-Based Authentication on a Real-World Large-Scale Online Service. In: ACM Transactions on Privacy and Security (2022). doi: 10.1145/3546069

Few (invalid) user agents strings from the original data set could not be parsed, so their device type is empty. Perhaps this parse error is useful information for your studies, so we kept these 1526 entries.↩︎

This respository contains the CLUE-LDS (CLoud-based User Entity behavior analytics Log Data Set). The data set contains log events from real users utilizing a cloud storage suitable for User Entity Behavior Analytics (UEBA). Events include logins, file accesses, link shares, config changes, etc. The data set contains around 50 million events generated by more than 5000 distinct users in more than five years (2017-07-07 to 2022-09-29 or 1910 days). The data set is complete except for 109 events missing on 2021-04-22, 2021-08-20, and 2021-09-05 due to database failure. The unpacked file size is around 14.5 GB. A detailed analysis of the data set is provided in [1]. The logs are provided in JSON format with the following attributes in the first level:

id: Unique log line identifier that starts at 1 and increases incrementally, e.g., 1. time: Time stamp of the event in ISO format, e.g., 2021-01-01T00:00:02Z. uid: Unique anonymized identifier for the user generating the event, e.g., old-pink-crane-sharedealer. uidType: Specifier for uid, which is either the user name or IP address for logged out users. type: The action carried out by the user, e.g., file_accessed. params: Additional event parameters (e.g., paths, groups) stored in a nested dictionary. isLocalIP: Optional flag for event origin, which is either internal (true) or external (false). role: Optional user role: consulting, administration, management, sales, technical, or external. location: Optional IP-based geolocation of event origin, including city, country, longitude, latitude, etc. In the following data sample, the first object depicts a successful user login (see type: login_successful) and the second object depicts a file access (see type: file_accessed) from a remote location:

{"params": {"user": "intact-gray-marlin-trademarkagent"}, "type": "login_successful", "time": "2019-11-14T11:26:43Z", "uid": "intact-gray-marlin-trademarkagent", "id": 21567530, "uidType": "name"}

{"isLocalIP": false, "params": {"path": "/proud-copper-orangutan-artexer/doubtful-plum-ptarmigan-merchant/insufficient-amaranth-earthworm-qualitycontroller/curious-silver-galliform-tradingstandards/incredible-indigo-octopus-printfinisher/wicked-bronze-sloth-claimsmanager/frantic-aquamarine-horse-cleric"}, "type": "file_accessed", "time": "2019-11-14T11:26:51Z", "uid": "graceful-olive-spoonbill-careersofficer", "id": 21567531, "location": {"countryCode": "AT", "countryName": "Austria", "region": "4", "city": "Gmunden", "latitude": 47.915, "longitude": 13.7959, "timezone": "Europe/Vienna", "postalCode": "4810", "metroCode": null, "regionName": "Upper Austria", "isInEuropeanUnion": true, "continent": "Europe", "accuracyRadius": 50}, "uidType": "ipaddress"} The data set was generated at the premises of Huemer Group, a midsize IT service provider located in Vienna, Austria. Huemer Group offers a range of Infrastructure-as-a-Service solutions for enterprises, including cloud computing and storage. In particular, their cloud storage solution called hBOX enables customers to upload their data, synchronize them with multiple devices, share files with others, create versions and backups of their documents, collaborate with team members in shared data spaces, and query the stored documents using search terms. The hBOX extends the open-source project Nextcloud with interfaces and functionalities tailored to the requirements of customers. The data set comprises only normal user behavior, but can be used to evaluate anomaly detection approaches by simulating account hijacking. We provide an implementation for identifying similar users, switching pairs of users to simulate changes of behavior patterns, and a sample detection approach in our github repo. Acknowledgements: Partially funded by the FFG project DECEPT (873980). The authors thank Walter Huemer, Oskar Kruschitz, Kevin Truckenthanner, and Christian Aigner from Huemer Group for supporting the collection of the data set. If you use the dataset, please cite the following publication: [1] M. Landauer, F. Skopik, G. Höld, and M. Wurzenberger. "A User and Entity Behavior Analytics Log Data Set for Anomaly Detection in Cloud Computing". 2022 IEEE International Conference on Big Data - 6th International Workshop on Big Data Analytics for Cyber Intelligence and Defense (BDA4CID 2022), December 17-20, 2022, Osaka, Japan. IEEE. [PDF]

This statistic shows the frequency of hot drink usage (except coffee or tea) in France in 2023. In 2023, an estimated 4.9 million people in France were heavy users of hot drinks.

Complementary services current results per user by type of service and type of institute. National.

This statistic shows the frequency of rum consumption in France in 2023, by user type. In 2023, an estimated 2.8 million people in France were heavy rum users.

DataIntelo recently published a report, titled Global User Experience (UX) Research Software Market Insights, Forecast to 2025. The research includes collation of data that is gathered using primary and secondary research methodologies. The research is conducted by professionals who have remarkable expertise in the field. The report elaborates on all the aspect of the market for a comprehensive understanding of the market dynamics. The market is divided into various segments and all the segments follow a similar format for a detailed explanation of the market.
In report covers both sales and revenue and studies the segments pertaining to application, products, services, and regions. To assess the market’s future the research report also discusses the competitive landscape present in the global User Experience (UX) Research Software market.
In 2018 the global User Experience (UX) Research Software market size was 130 million US$ and will reach 417.1 million US$ by 2025, with a CAGR of 18.2% during the forecast period.
Global User Experience (UX) Research Software Market: Scope of the Market
User Experience (UX) research is the process of discovering the behaviors, motivations and needs of your customers through observation, task analysis, and other types of user feedback.
The report first uses historic data from different companies. The data collected is used to analyses the growth of industries in the past years. It includes data from the year 2014 to the year 2019. The forecast data provides the reader with an understating of the future of the market. The same data is used to predict the expectation of the companies and how they are expected to evolve in the coming years. The research provides historical as well as estimated data from the year 2019 to 2025. The details in the report give a brief overview of the market by examining its historical data, the current data, and forecast data to understand the growth of the market.
Global User Experience (UX) Research Software Market: Segment Analysis
The report also outlines the sales and revenue generated by the global User Experience (UX) Research Software market. It is broken down in many segments, such as regional, country level, by type, application, and others. This enables a granular view of the market, focusing on the government policies that could change the dynamics. It also assesses the research and development plans of the companies for better product innovation.
The report is based on research done specifically on consumer goods. The goods have bifurcated depending on their use and type. The type segment contains all the necessary information about the different forms and their scope in the global User Experience (UX) Research Software market. The application segment defines the uses of the product. It points out the various changes that these products have been through over the years and the innovation that players are bringing in. The focus of the report on the consumer goods aspect helps in explaining changing consumer behavior that will impact the global User Experience (UX) Research Software market.
The main consumer market is located in developed countries. North America is the largest consumption region, with the total market share of 44.69% in 2018, and USA accounts most of the North America market, with the market share of 88.08%, and account the total market share of 39.36% in 2018. Followed by Europe, accounting for 32.70%. In the coming years there is an increasing demand for User Experience (UX) Research Software in the regions of APAC and Europe.
Global User Experience (UX) Research Software Market: Regional Segment Analysis
Based on region, the global User Experience (UX) Research Software market is segmented into North America and Europe.. Asia Pacific has a large population, which makes its market potential a significant one. It is the fastest-growing and most lucrative region in the global economy. This chapter specifically explains the impact of population on the global User Experience (UX) Research Software market. Research views it through a regional lens, giving the readers a microscopic understanding of the changes to prepare for.
The report covers different aspects of the market from a consumer goods point of view. It aims to be a guiding hand to interested readers for making profitable business decisions.
The following players are covered in this report:
UserTesting
Qualtrics
Hotjar
Lookback
UserZoom
Validately
Userlytics
UsabilityHub
TryMyUI
Woopra
Usabilla
TechSmith
20 | 20
User Interviews
User Experience (UX) Research Software Breakdown Data by Type
Cloud Based
On-Premises
User Experience (UX) Rese

1) Data Introduction • The Land-Use Scene Classification Dataset is an image dataset built to classify land-use types in different regions based on Landsat satellite imagery.

2) Data Utilization (1) Characteristics of the Land-Use Scene Classification Dataset: • The images are collected from a diverse range of geographic environments, including urban, rural, coastal, and forested areas, making the dataset suitable for evaluating domain generalization performance. • It is based on low-resolution Landsat satellite images, yet designed to effectively distinguish various terrain and structural patterns even with limited spatial resolution.

(2) Applications of the Land-Use Scene Classification Dataset: • Development of land-use classification models: The dataset can be used to train deep learning models that automatically classify land-use types such as residential areas, roads, and farmlands from satellite imagery. • GIS-based land-use change analysis: It can support geographic information system (GIS) research to analyze land-use pattern changes over time and infer spatial utilization trends.

Auto clustering information of rented car usage.

This dataset contains statistical data on the percentage of electronic cigarette users, categorized by gender and type of user, for the year 2013. The data includes information on the percentage of male and female users, as well as the overall percentage of users.

User And Entity Behavior Analytics Market size was valued at USD 1035.54 Million in 2024 and is projected to reach USD 11215.59 Million by 2031, growing at a CAGR of 40.50% from 2024 to 2031.

Global User And Entity Behavior Analytics Market Drivers

Growing Cybersecurity Risks: As a result of an increase in data breaches and cyberattacks, enterprises are depending more and more on UEBA solutions to identify and neutralize insider threats as well as advanced persistent threats (APTs).

Regulatory Compliance: Organizations are using UEBA solutions to ensure compliance with data protection and privacy legislation due to stringent regulatory requirements, including GDPR, HIPAA, and others.

Growing Adoption of Cloud Services: As more businesses shift their data and apps to the cloud, there's a growing demand for UEBA solutions that can secure and monitor cloud settings.

Focus on Insider Threats: Due to the fact that insider threats can be harder to identify than external threats, organizations are realizing the importance of monitoring and analyzing user and entity behavior in order to identify and stop insider attacks.

Developments in AI and Machine Learning: Organizations seeking more sophisticated threat detection capabilities are adopting UEBA solutions because they utilize AI and machine learning algorithms to analyze large volumes of data and identify abnormalities in user and entity behavior.

No. denotes the number of individuals moving through a gate. This is then expressed as a percentage broken down by their reason for being on the ward.

The data contains trajectories and basic information about road users in a shared space.

Trajectories are denoted with time step, object id, x-coordinate, y-coordinate, and user type. Each time step last 0.5 seconds. Coordinates are in pixel and can be transferred to real-world scale using the scale provided. There are three types of users: pedestrian, cyclist, and vehicle.

In addition, the data also provides demographic attributes for road users. Pedestrians walking in groups are annotated.

This statistic shows the frequency of air freshener, scented candle and fabric freshener usage in France from 2014 to 2020. In 2020, an estimated 1.4 million people in France were heavy users of air fresheners, scented candles and fabric fresheners.

Canadian Internet use survey, Internet use, by location of access and household type for Canada, urban area or rural area from 2005 to 2009. (Terminated)

Analysis of ‘Star dataset to predict star types’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/deepu1109/star-dataset on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Dataset Info:

This is a dataset consisting of several features of stars.

Some of them are: - Absolute Temperature (in K) - Relative Luminosity (L/Lo) - Relative Radius (R/Ro) - Absolute Magnitude (Mv) - Star Color (white,Red,Blue,Yellow,yellow-orange etc) - Spectral Class (O,B,A,F,G,K,,M) - Star Type **(Red Dwarf, Brown Dwarf, White Dwarf, Main Sequence , SuperGiants, HyperGiants)**

Lo = 3.828 x 10^26 Watts (Avg Luminosity of Sun) Ro = 6.9551 x 10^8 m (Avg Radius of Sun)

Purpose:

The purpose of making the dataset is to prove that the stars follows a certain graph in the celestial Space , specifically called Hertzsprung-Russell Diagram or simply HR-Diagram so that we can classify stars by plotting its features based on that graph.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3791628%2F14338bbebf77d18e1faef582bccdbdd6%2Fhr.jpg?generation=1597349509841965&alt=media" alt="hr-1"> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3791628%2F9fc57334a9b9fafbc71aacdd6e5cd69c%2F310px-Hertzsprung-Russel_StarData.png?generation=1597349661801284&alt=media" alt="hr-2">

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F3791628%2Ffe9436bf4e2d23b5b18fb3db1f1fcbcb%2FHRDiagram.png?generation=1597348809674507&alt=media" alt="hr-3">

Data Collection and Preparation techniques:

The dataset is created based on several equations in astrophysics. They are given below:

1. Stefan-Boltzmann's law of Black body radiation (To find the luminosity of a star)
2. Wienn's Displacement law (for finding surface temperature of a star using wavelength)
3. Absolute magnitude relation
4. Radius of a star using parallax .

The dataset took 3 weeks to collect for 240 stars which are mostly collected from web. The missing data were manually calculated using those equations of astrophysics given above.

Acknowledgements:

• Wikipedia
• Stars and Galaxies by SEEDS | Backman

Repo Link:

AstroML

--- Original source retains full ownership of the source dataset ---