IPinfo IP to Country ASN database

IPinfo's IP to Country ASN database is an open-access database that provides information on the country and ASN (Autonomous System Number) of a given IP address.

• It offers full accuracy and is updated daily.
• The database is licensed under CC-BY-SA 4.0, allowing for commercial usage.
• It includes both IPv4 and IPv6 addresses.
• There are two file formats available: CSV and MMDB.

Notebook

Please explore the provided notebook to learn about the dataset:

🔗 IPinfo IP to Country ASN Demo Notebook for Kaggle

Documentation

Detailed documentation for the IP to Country ASN database can be found on IPinfo's documentation page. Database samples are also available on IPinfo's GitHub repo.

🔗 Documentation: https://ipinfo.io/developers/ip-to-country-asn-database

Field Name	Example	Description
start_ip	194.87.139.0	The starting IP address of an IP address range
end_ip	194.87.139.255	The ending IP address of an IP address range
country	NL	The ISO 3166 country code of the location
country_name	Netherlands	The name of the country
continent	EU	The continent code of the country
continent_name	Europe	The name of the continent
asn	AS1239	The Autonomous System Number
as_name	Sprint	The name of the AS (Autonomous System) organization
as_domain	sprint.net	The official domain or website of the AS organization

Context and value

The IPinfo IP to Country ASN database is a subset of IPinfo's IP to Geolocation database and the ASN database.

The database provides daily updates, complete IPv4 and IPv6 coverage, and full accuracy, just like its parent databases. The database is crucial for:

• Cybersecurity and threat intelligence
• Open Source Intelligence (OSINT)
• Firewall policy configuration
• Sales intelligence
• Marketing analytics and adtech
• Personalized user experience

Whether you are running a web service or a server connected to the internet, this enterprise-ready database should be part of your tech stack.

Usage

In this dataset, we include 3 files:

• country_asn.csv → For reverse IP look-ups and running IP-based analytics
• country_asn.mmdb → For IP address information look-ups
• ips.txt → Sample IP addresses

Using the CSV dataset

As the CSV dataset has a relatively small size (~120 MB), any dataframe and database should be adequate. However, we recommend users not use the CSV file for IP address lookups. For everything else, feel free to explore the CSV file format.

Using the MMDB dataset

The MMDB dataset requires a special third-party library called the MMDB reader library. The MMDB reader library enables you to look up IP addresses at the most efficient speed possible. However, as this is a third-party library, you should install it via pip install in your notebook, which requires an internet connection to be enabled in your notebook settings.

Please see our attached demo notebook for usage examples.

IP to Country ASN provides many diverse solutions, so we encourage and share those ideas with the Kaggle community!

Sources

The geolocation data is produced by IPinfo's ProbeNet, a globe-spanning probe network infrastructure with 400+ servers. The ASN data is collected from public datasets like WHOIS, Geofeed etc. The ASN data is later parsed and structured to make it more data-friendly.

See the Data Provenance section below to learn more.

Please note that this Kaggle Dataset is not updated daily. We recommend users download our free IP to Country ASN database from IPinfo's website directly for daily updates.

Terminology

AS Organization - An AS (Autonomous System) organization is an organization that owns a block or range of IP addresses. These IP addresses are sold to them by the Regional Internet Organizations (RIRs). Even though this AS organization may own an IP address, they sometimes do not operate IP addresses directly and may rent them out to other organizations. You can check out our IP to Company data or ASN database to learn more about them.

ASN - ASN or Autonomous System Number is the unique identifying number assigned to an AS organization.

IP to ASN - Get ASN and AS organizat...

Geo Open is an IP address geolocation per country in MMDB format. The database can be used as a replacement for software using the MMDB format. Information about MMDB format: https://maxmind.github.io/MaxMind-DB/ Open source server using Geo Open: https://github.com/adulau/mmdb-server Open source library to read MMDB file: https://github.com/maxmind/MaxMind-DB-Reader-python Historical dataset: https://cra.circl.lu/opendata/geo-open/ The database is automatically generated from public BGP AS announces matching the country code. The precision is at country level.

A collection of router interface IP addresses geolocated to the city level. 11,857 IP addressed geolocated based on DNS names and 4,838 IP addresses geolocated based on RTT proximity to RIPE Atlas probes. The DNS-based data was created on May 15, 2016. The RTT-proximity data was created from measurements collected on May 25, 2016. The total number of addresses in the dataset is 16586 (109 addresses found to be common between the two sources of data with very similar locations). Data supplement for paper M. Gharaibeh, A. Shah, B. Huffaker, H. Zhang, R. Ensafi, and C. Papadopoulos, A Look at Router Geolocation in Public and Commercial Databases, Proc. Internet Measurement Conference (IMC), Nov 2017.

Our IP to geolocation data delivers large-scale digital and location intelligence, refreshed in real-time to power critical business functions. With our data, you can connect an IP address to a precise geographic location, enabling you to build powerful, location-aware applications and derive deep consumer insights.

Leverage our IP to geolocation data solutions for the following use cases: - Targeted, Data-Driven Advertising - Data Validation & Model Building - Travel & Location-Based Targeting - Fraud Detection & Prevention

With AdPreference, expect the following key benefits through our partnership: - Augment Data Attributes - Enrich CRM - Personalize Audience - Audience Curation

Access the largest and most customizable IP to geolocation data with AdPreference. Supercharge your needs with unique and enriched IP to geolocation intelligence not found anywhere else.

For more information, please visit https://www.adpreference.co/

IP Geolocation Solution Market Outlook

The global IP geolocation solution market size was valued at approximately USD 2.3 billion in 2023 and is expected to reach an estimated USD 6.8 billion by 2032, growing at a compound annual growth rate (CAGR) of around 12.7% during the forecast period. This impressive growth can be attributed to the increasing need for precise geolocation data for various applications, including fraud detection, content personalization, and geomarketing.

One of the primary growth factors driving the IP geolocation solution market is the rising demand for enhanced cybersecurity measures. With the proliferation of digital platforms and online services, the need for secure and accurate geolocation data has become crucial to counteract cyber threats such as fraud and identity theft. Organizations are increasingly deploying IP geolocation solutions to verify user locations and detect suspicious activities, thereby safeguarding sensitive information and maintaining trust among their users.

Another significant growth driver is the expanding adoption of IP geolocation solutions in digital marketing and advertising. Businesses are leveraging geolocation data to create targeted and personalized marketing campaigns, which are more effective in reaching the right audience. By understanding the geographical context of their users, companies can tailor their content and advertisements to match local preferences and trends, resulting in higher engagement and conversion rates. This trend is particularly evident in the retail sector, where location-based marketing is proving to be a game-changer.

The rise of remote work and online gaming has also contributed to the market's growth. With more people working from home and engaging in online gaming, the demand for reliable IP geolocation solutions has surged. These solutions help ensure compliance with regional regulations, optimize gaming experiences by reducing latency, and enhance user safety by preventing unauthorized access. Additionally, the growth of the e-commerce sector has necessitated the use of geolocation data to improve logistics and delivery services, further propelling market demand.

Regionally, North America holds a significant share in the IP geolocation solution market, primarily due to the presence of major technology companies and early adoption of advanced technologies. Europe and Asia Pacific are also witnessing substantial growth, driven by increased investments in digital infrastructure and rising awareness about the benefits of geolocation solutions. The Asia Pacific region, in particular, is expected to exhibit the highest growth rate during the forecast period, fueled by the rapid digital transformation and expanding internet user base in countries like China and India.

Component Analysis

The IP geolocation solution market is segmented into software and services components. The software segment encompasses various geolocation software tools that are designed to provide precise location data based on IP addresses. This segment is expected to dominate the market due to the continuous advancements in software technology and the increasing deployment of IP geolocation software across various industries. These software solutions offer real-time location tracking, enhanced security features, and integration capabilities with other systems, making them indispensable for modern businesses.

On the other hand, the services segment includes professional services such as consulting, integration, and maintenance services. With the growing complexity of IP geolocation systems, businesses often require expert assistance to effectively implement and manage these solutions. Service providers offer valuable support in configuring geolocation tools, ensuring seamless integration with existing infrastructure, and providing ongoing maintenance to keep the systems running smoothly. The demand for these services is particularly high among small and medium enterprises (SMEs) that may lack the in-house expertise to handle geolocation solutions independently.

Moreover, the services segment is expected to witness significant growth due to the increasing trend of outsourcing geolocation-related tasks to specialized service providers. By outsourcing, businesses can focus on their core operations while leveraging the expertise of professionals to manage their geolocation needs. This trend is further driven by the rise of managed service providers (MSPs) that offer comprehensive geolocation solutions as part of their service portfolio, catering to the diverse requirem

IP Geolocation Services Market Outlook

According to our latest research, the global IP Geolocation Services market size in 2024 stands at USD 2.4 billion, reflecting robust adoption across diverse industries. The market is projected to grow at a CAGR of 12.8% from 2025 to 2033, reaching an estimated USD 7.1 billion by 2033. This rapid growth is primarily driven by increasing demand for location-based analytics, enhanced cybersecurity, and personalized digital experiences across sectors. The proliferation of digital transformation initiatives, coupled with the rising sophistication of cyber threats, is compelling organizations to integrate advanced IP geolocation services into their operational and security frameworks. These trends underscore the pivotal role of IP geolocation in modern business environments, as organizations seek to leverage location intelligence for competitive advantage and regulatory compliance.

One of the primary growth factors for the IP Geolocation Services market is the surge in demand for fraud detection and prevention solutions. As businesses expand their digital footprints, the need to authenticate user locations and detect anomalies in real-time has become increasingly critical. Financial institutions, e-commerce platforms, and online service providers are leveraging IP geolocation to identify suspicious activity, mitigate risks, and comply with stringent regulatory requirements. The integration of artificial intelligence and machine learning with IP geolocation technologies has further enhanced the accuracy and efficiency of fraud detection systems, enabling proactive threat mitigation. This evolution is particularly significant in sectors such as BFSI and retail, where the cost of data breaches and fraudulent transactions can be substantial, driving sustained investment in advanced geolocation solutions.

Another key driver is the escalating demand for content personalization and targeted advertising. Organizations are increasingly utilizing IP geolocation data to deliver tailored content, optimize user experiences, and increase engagement rates. By understanding the geographic context of users, companies can localize content, adapt marketing strategies, and ensure compliance with regional regulations. This capability is especially valuable for global enterprises seeking to navigate the complexities of cross-border digital marketing and data privacy laws. The adoption of cloud-based IP geolocation services has further democratized access to these capabilities, allowing small and medium enterprises to compete effectively with larger counterparts in delivering personalized and context-aware services to their customers.

Additionally, the growing emphasis on network security and compliance is fueling the adoption of IP geolocation services worldwide. Enterprises are increasingly concerned about securing their digital assets against unauthorized access and cyberattacks originating from specific geographic regions. IP geolocation enables organizations to implement geo-fencing, restrict access based on location, and monitor network traffic for suspicious activities. Regulatory mandates such as GDPR, CCPA, and other regional data protection laws require organizations to track and manage data flows based on user locations, further amplifying the need for accurate and reliable geolocation solutions. As organizations strive to build resilient and compliant digital infrastructures, the demand for comprehensive IP geolocation services is expected to witness sustained growth over the forecast period.

Geolocation Fraud Detection has emerged as a critical component in the arsenal of tools used by organizations to combat cyber threats. As digital transactions become more prevalent, the sophistication of fraudulent activities has increased, making it imperative for businesses to adopt advanced geolocation technologies. These technologies allow companies to pinpoint the location of users with high precision, enabling them to identify and block potentially fraudulent transactions in real-time. By leveraging geolocation data, businesses can enhance their fraud detection capabilities, ensuring that only legitimate transactions are processed. This not only protects the financial interests of companies but also builds trust with customers, who are increasingly concerned about the security of their online interactions. The integration of geolocat

IP Geolocation Services Market Outlook

According to our latest research, the global IP Geolocation Services market size reached USD 2.1 billion in 2024, demonstrating robust momentum across various industry verticals. The market is projected to grow at a CAGR of 13.2% from 2025 to 2033, reaching a forecasted value of USD 6.2 billion by 2033. This impressive growth trajectory is fueled by the increasing demand for advanced cybersecurity solutions, regulatory compliance requirements, and the rising need for personalized content delivery in a digital-first business landscape.

One of the primary growth drivers for the IP Geolocation Services market is the growing sophistication and frequency of cyber threats targeting enterprises globally. As organizations digitize their operations and expand their online presence, the need to accurately identify, monitor, and manage user locations has become critical for preventing fraud, detecting suspicious activities, and securing sensitive data. IP geolocation services empower businesses to implement real-time risk assessment protocols by pinpointing user locations, thereby mitigating the risk of unauthorized access and fraudulent transactions. This capability is particularly vital for sectors such as banking, financial services, and insurance (BFSI), where secure authentication and transaction monitoring are paramount. Furthermore, the proliferation of cloud computing and the Internet of Things (IoT) has led to a surge in connected devices, creating a larger attack surface and further emphasizing the need for robust IP geolocation solutions.

Another significant factor propelling the IP Geolocation Services market is the increasing emphasis on delivering personalized digital experiences. Enterprises across industries are leveraging geolocation data to tailor content, advertisements, and services according to user preferences and regional trends. This targeted approach not only enhances customer engagement but also drives higher conversion rates and improves brand loyalty. For instance, e-commerce platforms utilize IP geolocation to display localized offers, adjust pricing strategies, and ensure compliance with regional regulations. Similarly, media and entertainment companies use geolocation data to manage content licensing and deliver region-specific programming. As consumer expectations for seamless and relevant online experiences continue to rise, the adoption of IP geolocation services is expected to accelerate further.

Regulatory compliance is also a key catalyst for market growth, especially in sectors handling sensitive user data. Governments and regulatory bodies worldwide are enforcing stringent data privacy laws, such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States. These regulations mandate organizations to implement measures that ensure data localization, user consent, and robust access controls based on geographical boundaries. IP geolocation services play a crucial role in enabling enterprises to comply with these requirements by accurately determining user locations, restricting access where necessary, and maintaining detailed audit trails. As regulatory frameworks evolve and expand to cover emerging digital ecosystems, the demand for advanced IP geolocation solutions is set to witness sustained growth.

From a regional perspective, North America currently leads the global IP Geolocation Services market, driven by the presence of major technology providers, high digital adoption rates, and a mature regulatory environment. However, Asia Pacific is anticipated to exhibit the fastest growth over the forecast period, supported by rapid digital transformation, expanding internet penetration, and increasing investments in cybersecurity infrastructure. Europe remains a significant market due to its strong emphasis on data privacy and compliance. Meanwhile, Latin America and the Middle East & Africa are gradually emerging as promising markets, fueled by growing e-commerce activity and the need for advanced fraud prevention mechanisms.

Component Analysis

The IP Geolocation Services market is segmented by component into software and services, each playing a pivotal role in the adoption and implementation of geolocation solutions. The software segment currently holds the largest market share, owing to the widespread deployment of geolocation APIs, SDKs, and integrated platforms that faci

Global IP Geolocation Solutions Market is segmented by Application (Digital advertising_ Cybersecurity_ E-commerce), Type (IP geolocation API_ Data enrichment services_ Real-time location tracking), and Geography (North America_ LATAM_ West Europe_Central & Eastern Europe_ Northern Europe_ Southern Europe_ East Asia_ Southeast Asia_ South Asia_ Central Asia_ Oceania_ MEA)

Our IP to geolocation data delivers large-scale digital and location intelligence, refreshed in real-time to power critical business functions. With our data, you can connect an IP address to a precise geographic location, enabling you to build powerful, location-aware applications and derive deep consumer insights.

Leverage our IP to geolocation data solutions for the following use cases: - Targeted, Data-Driven Advertising - Data Validation & Model Building - Travel & Location-Based Targeting - Fraud Detection & Prevention

With AdPreference, expect the following key benefits through our partnership: - Augment Data Attributes - Enrich CRM - Personalize Audience - Audience Curation

Access the largest and most customizable IP to geolocation data with AdPreference. Supercharge your needs with unique and enriched IP to geolocation intelligence not found anywhere else.

For more information, please visit https://www.adpreference.co/

This dataset contains all data collected by the CyberLab honeynet experiment, from May 2019 to February 2020.

The experiment was based on the Cowrie honeypot (https://github.com/cowrie/cowrie, versions 1.6.0 and 2.0.2, see below for the timeline) deployed on approximately 50 nodes at different EU and US universities and companies. This number has varied throughout the duration of the experiment due to scaling efforts and the target node availability. All public IP addresses in the dataset are pseudonymized to protect the identity of the destination nodes.

Each file in the dataset is a daily compilation of all connections starting at midnight on that date (date in filename, midnight in UTC time), grouped into "attack sessions". Each event in such a session includes all the data reported by the honeypot software (https://github.com/cowrie/cowrie). The honeypot has been operating in its default (low-interaction) mode using version 1.6.0 from the start of the experiment until November 8, 2019; after that date, we upgraded to Cowrie version 2.0.2, which allowed us to back it by a pool of real Linux instances to provide more convincing high-interaction mode. Results from high-interaction mode are tagged with "sensor:ubuntu_basic_pool".

Geolocation data was added to Cowrie output messages based on the source IP address.

Field Description =============================== =========================================================== session_id Unique ID of the session dst_ip_identifier Pseudonymized dst public IPv4 of the honeypot node dst_host_identifier Obfuscated (pseudonymized) name of the honeypot node src_ip_identifier Obfuscated (pseudonymized) IP address of the attacker eventid Event id of the session in the cowrie honeypot timestamp UTC time of the event message Message of the Cowrie honeypot protocol Protocol used in the cowrie honeypot; either ssh or telnet geolocation_data/postal_code Source IP postal code as (determined by logstash) geolocation_data/continent_code Source IP continent code (as determined by logstash) geolocation_data/country_code3 Source IP country code3 (as determined by logstash) geolocation_data/region_name Source IP region name (as determined by logstash) geolocation_data/latitude Source IP latitude (as determined by logstash) geolocation_data/longitude Source IP longitude (as determined by logstash) geolocation_data/country_name Source IP full country name (as determined by logstash) geolocation_data/timezone Source IP timezone geolocation_data/country_code2 Source IP country code2 geolocation_data/region_code Source IP region code geolocation_data/city_name Source IP city name src_port Source TCP port sensor Sensor name; serves to identify our experiment config arch Represents the CPU/OS architecture emulated by cowrie duration Session duration in seconds ssh_client_version Attacker's SSH client version username Login username; only used for login events password Password; only used for login events macCS HMAC algorithms supported by the client encCS Encryption algorithms supported by the client kexAlgs Key exchange algorithms supported by the client keyAlgs Public key algorithms supported by the client

More detailed description of the fields (with examples) and all subsequent data (after February 2020) can be found at cyber.ltfe.org.

BASE YEAR	2024
HISTORICAL DATA	2019 - 2023
REGIONS COVERED	North America, Europe, APAC, South America, MEA
REPORT COVERAGE	Revenue Forecast, Competitive Landscape, Growth Factors, and Trends
MARKET SIZE 2024	744.1(USD Million)
MARKET SIZE 2025	776.9(USD Million)
MARKET SIZE 2035	1200.0(USD Million)
SEGMENTS COVERED	Application, Deployment Type, End User, Features, Regional
COUNTRIES COVERED	US, Canada, Germany, UK, France, Russia, Italy, Spain, Rest of Europe, China, India, Japan, South Korea, Malaysia, Thailand, Indonesia, Rest of APAC, Brazil, Mexico, Argentina, Rest of South America, GCC, South Africa, Rest of MEA
KEY MARKET DYNAMICS	Increasing cybersecurity threats, Growing demand for location-based services, Rising e-commerce activities, Need for fraud prevention, Adoption of IoT devices
MARKET FORECAST UNITS	USD Million
KEY COMPANIES PROFILED	IP2Location, IPAddressGuide, MaxMind, IPGeolocation, Online IP Address Lookup by hostname, WhoisXML API, IPInfo, NeoGeo, Geolocation by ipinfo, DBIP, DataTooltip, Ipdata, IPstack, CountryIPBlocks
MARKET FORECAST PERIOD	2025 - 2035
KEY MARKET OPPORTUNITIES	Rising demand for cybersecurity solutions, Growth in digital marketing analytics, Increased demand for geolocation services, Expansion of IoT devices, Need for compliance with data regulations
COMPOUND ANNUAL GROWTH RATE (CAGR)	4.4% (2025 - 2035)

The Near Real Time (NRT) VIIRS/JPSS2 Moderate Resolution Terrain Corrected Geolocation 6-Min L1 Swath, short-name VJ203MOD_NRT) is the Joint Polar-orbiting Satellite System-2 (JPSS-2/NOAA-21) platform-based NASA VIIRS L1 terrain-corrected geolocation product, and contains the derived line-of-sight (LOS) vectors for each of the 750-m moderate-resolution, or M-bands. The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform's ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It produces geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VJ203MOD product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, and quality flag for every pixel location. VJ203MOD provides a fundamental input to derive a number of VIIRS M-band higher-level products.The J2 VIIRS geolocation underwent an on-orbit validation. Geolocation errors of about 350 m in the along-scan direction and about 165 m in the along-track direction were corrected for the image-resolution bands and moderate-resolution bands. The Day-Night band (DNB) geolocation error of about 2000 m was corrected. Further, the geolocation biases in the scan profile were also corrected. All these corrections bring the geolocation uncertainties for the J2 L1 products to within 75 m (1-sigma) in both the along-scan and along-track directions.

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

1. 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.↩︎

Location Intelligence Market Outlook

The global location intelligence market is poised to experience substantial growth, with a market size valued at approximately USD 15 billion in 2023, projected to reach USD 45 billion by 2032, reflecting a robust compound annual growth rate (CAGR) of around 13%. This growth trajectory is attributed to the increasing adoption of location-based services across various industries which are propelling the need for sophisticated analysis and interpretation of geographical data. The primary growth drivers include technological advancements in geospatial analytics, increased usage of smart devices, and the rising demand for efficient business operations through location-based insights.

Technological advancements have played a pivotal role in the proliferation of location intelligence solutions. The evolution of spatial data technologies, such as Geographic Information Systems (GIS), GPS advancements, and enhanced data analytics platforms, has revolutionized the way businesses interpret and utilize geographical data. The integration of Artificial Intelligence (AI) and Machine Learning (ML) has further enhanced the capabilities of location intelligence software, allowing for more accurate predictions and real-time data analysis. These innovations are enabling enterprises to make more informed decisions, optimizing operational efficiency and customer engagement strategies.

Another significant growth factor is the increasing penetration of smart devices and the Internet of Things (IoT). The proliferation of smartphones and connected devices generates vast amounts of location-based data that businesses can leverage to gain deeper insights into consumer behavior and preferences. This trend is further supported by increasing internet connectivity and digital transformation across sectors, which is facilitating the deployment of location-based services and solutions on a larger scale. As businesses continue to explore new ways to enhance customer experience and improve service delivery, location intelligence solutions are becoming an integral component of strategic planning and decision-making.

The rising demand for operational efficiency and business optimization is also propelling the growth of the location intelligence market. Organizations across various sectors, such as retail, transportation, and logistics, are increasingly leveraging location-based insights to streamline operations, reduce costs, and improve service delivery. For instance, businesses are using location analytics to optimize supply chain management, enhance fleet management, and improve route planning, leading to significant cost savings and improved operational efficiency. Furthermore, location intelligence solutions are aiding companies in identifying new market opportunities and enhancing customer engagement through targeted marketing strategies.

IP Geolocation Solutions are becoming increasingly vital in the realm of location intelligence, offering businesses the ability to pinpoint the geographical location of their online users. This capability is crucial for enhancing customer engagement and personalizing user experiences. By integrating IP geolocation data with existing location intelligence systems, companies can gain a more comprehensive understanding of user behavior and preferences. This integration allows for the development of targeted marketing strategies and improved service delivery, ultimately leading to increased customer satisfaction and loyalty. As businesses continue to prioritize customer-centric approaches, the demand for IP Geolocation Solutions is expected to grow, further driving the expansion of the location intelligence market.

Regionally, North America is expected to dominate the location intelligence market, driven by the presence of key market players and early adoption of advanced technologies. The Asia Pacific region is anticipated to witness the highest growth rate, fueled by rapid urbanization, increasing smartphone penetration, and the growing adoption of digital services. Europe is also expected to contribute significantly to market growth, supported by technological advancements and widespread adoption across industries. The Middle East & Africa and Latin America regions are projected to experience moderate growth, driven by increasing investments in digital infrastructure and rising demand for location-based services.

Component Analysis

The location intelligence market is segmented by component into soft

The Near Real Time (NRT) VIIRS/JPSS2 Day/Night Band Resolution Terrain Corrected Geolocation 6-Min L1 Swath 750m, short-name VJ203DNB_NRT is the Joint Polar-orbiting Satellite System-2 (JPSS-2/NOAA-21) platform-based NASA VIIRS L1 terrain-corrected geolocation product, and contains the derived line-of-sight (LOS) vectors for the single panchromatic Day-Night band (DNB). The geolocation algorithm uses a number of inputs that include an Earth ellipsoid, geoid, and a digital terrain model along with the SNPP platform's ephemeris and attitude data, and knowledge of the VIIRS sensor and satellite geometry. It provides geodetic coordinates (latitude and longitude), and related parameters for each VIIRS L1 pixel. The VJ203DNB product includes geodetic latitude, longitude, surface height above the geoid, solar zenith and azimuth angles, lunar zenith and azimuth angles, sensor zenith and azimuth angles, land/water mask, moon illumination fraction and phase angle, and quality flag for every pixel location.

The J2 VIIRS geolocation underwent an on-orbit validation. Geolocation errors of about 350 m in the along-scan direction and about 165 m in the along-track direction were corrected for the image-resolution bands and moderate-resolution bands. The Day-Night band (DNB) geolocation error of about 2000 m was corrected. Further, the geolocation biases in the scan profile were also corrected. All these corrections bring the geolocation uncertainties for the J2 L1 products to within 75 m (1-sigma) in both the along-scan and along-track directions.

Location Verification Software Market Outlook

According to our latest research, the global Location Verification Software market size reached USD 9.2 billion in 2024, with a robust compound annual growth rate (CAGR) of 14.7% projected from 2025 to 2033. By the end of the forecast period, the market is expected to reach approximately USD 29.4 billion by 2033. The remarkable growth of this market is primarily driven by increasing concerns around digital fraud, rising regulatory compliance requirements, and the proliferation of location-based services across diverse industries. The rapid adoption of mobile devices and IoT-enabled solutions, coupled with the surge in remote working and e-commerce, has further fueled the demand for advanced location verification solutions worldwide.

A significant growth factor for the Location Verification Software market is the escalating threat of cyber fraud and identity theft, particularly in sensitive sectors such as banking, financial services, and insurance (BFSI). As digital transactions and online interactions become more prevalent, organizations are under immense pressure to ensure the authenticity of users’ locations to prevent fraudulent activities. Location verification software leverages technologies such as GPS, IP address tracking, Wi-Fi triangulation, and mobile network data to provide real-time validation of user locations, thereby minimizing the risk of unauthorized access and transaction fraud. This heightened security need is prompting businesses to invest heavily in robust location verification solutions, further propelling market expansion.

Another key driver for the market is the stringent regulatory landscape, especially in regions like North America and Europe, where data privacy and compliance mandates have become increasingly rigorous. Regulatory frameworks such as GDPR, PSD2, and CCPA necessitate that organizations implement advanced verification mechanisms to safeguard sensitive data and ensure compliance. Location verification software plays a pivotal role in helping enterprises meet these requirements by providing auditable records of user locations, supporting compliance management, and enabling transparent reporting. This regulatory push is compelling organizations across sectors—including healthcare, government, and retail—to integrate location verification tools into their digital ecosystems, thereby stimulating market growth.

Furthermore, the growing adoption of location-based marketing and personalized customer engagement strategies is contributing to the expansion of the Location Verification Software market. Businesses are increasingly leveraging geomarketing and targeted advertising to enhance user experiences and drive revenue. Accurate location data, validated by sophisticated software solutions, enables organizations to deliver contextually relevant content, offers, and services, thereby improving customer satisfaction and loyalty. The convergence of artificial intelligence, machine learning, and big data analytics with location verification platforms is also enabling more precise and scalable solutions, opening new avenues for market players to innovate and differentiate their offerings.

From a regional perspective, North America currently dominates the Location Verification Software market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The high concentration of technology-driven enterprises, mature regulatory frameworks, and robust digital infrastructure in these regions have accelerated the adoption of location verification solutions. Meanwhile, emerging economies in Asia Pacific and Latin America are witnessing rapid market growth, fueled by increasing digitalization, expanding e-commerce activities, and rising awareness about cybersecurity threats. The Middle East & Africa region, though still nascent, is anticipated to experience steady growth as governments and businesses invest in digital transformation and fraud prevention initiatives.

Component Analysis

The Component segment of the Location Verification Software market is bifurcated into software and services. Software solutions constitute the backbone of this segment, encompassing a wide range of platforms, applications, and analytics tools designed to verify and authenticate user locations in real-time. These solutions utilize a blend of GPS, IP geolocation, mobile network triangulation, and Wi-Fi p

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The Near Real Time (NRT) geolocation fields are calculated for each 1 km MODIS Instantaneous Field of Views (IFOV) for all orbits daily. The locations and ancillary information corresponds to the intersection of the centers of each IFOV from 10 detectors in an ideal 1 km band on the Earth's surface. A digital terrain model is used to model the Earth's surface. The main inputs are the spacecraft attitude and orbit, the instrument telemetry and the digital elevation model. The geolocation fields include geodetic Latitude, Longitude, surface height above geoid, solar zenith and azimuth angles, satellite zenith and azimuth angles, and a land/sea mask for each 1 km sample. Additional information is included in the header to enable the calculation of the approximate location of the center of the detectors of any of the 36 MODIS bands. This product is used as input by a large number of subsequent MODIS products, particularly the products produced by the Land team.The shortname for this product is MYD03.