Version 3 with 517M hashes and counts of password usage ordered by most to least prevalent Pwned Passwords are 517,238,891 real world passwords previously exposed in data breaches. This exposure makes them unsuitable for ongoing use as they re at much greater risk of being used to take over other accounts. They re searchable online below as well as being downloadable for use in other online system. The entire set of passwords is downloadable for free below with each password being represented as a SHA-1 hash to protect the original value (some passwords contain personally identifiable information) followed by a count of how many times that password had been seen in the source data breaches. The list may be integrated into other systems and used to verify whether a password has previously appeared in a data breach after which a system may warn the user or even block the password outright.

The list contains every wordlist, dictionary, and password database leak that I could find on the internet (and I spent a LOT of time looking). It also contains every word in the Wikipedia databases (pages-articles, retrieved 2010, all languages) as well as lots of books from Project Gutenberg. It also includes the passwords from some low-profile database breaches that were being sold in the underground years ago. The format of the list is a standard text file sorted in non-case-sensitive alphabetical order. Lines are separated with a newline " " character. You can test the list without downloading it by giving SHA256 hashes to the free hash cracker or to @PlzCrack on twitter. Here s a tool for computing hashes easily. Here are the results of cracking LinkedIn s and eHarmony s password hash leaks with the list. The list is responsible for cracking about 30% of all hashes given to CrackStation s free hash cracker, but that figure should be taken with a grain of salt because s

The password management market was valued at slightly above two billion U.S. dollars in 2022 and was expected to exceed three billion U.S. dollars in revenue by 2025. However, the past few years have seen the emergence of new and more secure authentication methods such as passwordless security or multi-factor authentication. Despite the instability that password security currently has, it remains the most used security method worldwide to grant access to company infrastructure.

Password security

When it comes to online credentials storage habits in 2021, the most common method is still to remember them by heart or write them down on paper. It is no wonder that individuals find it more challenging to keep track of all their online accounts nowadays. Consequently, people tend to reuse the same credentials when creating new online accounts in order to make passwords easier to remember. In 2021, the most frequent password encountered over a 45-day period across the Internet of Things (IoT) devices was admin, used nearly 21 million times.

Multi-factor authentication (MFA)

Simply put, MFA grants the user access to a website or application only after successfully presenting at least two pieces of evidence that could identify the user to an authentication mechanism. Depending on the desired security level, MFA can provide several authentication types. One of the most common types of MFA used by companies worldwide is an SMS code or a one-time password (OTP). In 2021, Philippines registered the highest increase in MFA volume, at over 150 percent. By contrast, Israel’s MFA volume dropped by 70 percent.

WIFI-HANDSHAKE: Analysis of password patterns in Wi-Fi networks Adrian-Carballal, J. Pablo Galego-Carro, Nereida Rodriguez-Fernandez and Carlos Fernandez-Lozano PeerJ Computer Science

This paper seeks to provide a snapshot of the security of Wi-Fi access points in the metropolitan area of A Coruña. First, we discuss the options for obtaining a tool that allows the collection and storage of auditable information from Wi-Fi networks, from location to signal strength, security protocol or the list of connected clients. Subsequently, an analysis is carried out aimed at identifying password patterns in Wi-Fi networks with WEP, WPA and WPA2 security protocols. For this purpose, a password recovery tool called Hashcat was used to execute dictionary or brute force attacks, among others, with various word collections. The coverage of the access points in which passwords were decrypted is displayed on a heat map that represents various levels of signal quality depending on the signal strength. From the handshakes obtained, and by means of brute force, we will try to crack as many passwords as possible in order to create a targeted and contextualized dictionary both by geographical location and by the nature of the owner of the access point. Finally, we will propose a contextualized grammar that minimizes the size of the dictionary with respect to the most used ones and unifies the decryption capacity of the combination of all of them.

CITATION:

Carballal A, Galego-Carro JP, Rodriguez-Fernandez N, Fernandez-Lozano C. 2022. Wi-Fi Handshake: analysis of password patterns in Wi-Fi networks. PeerJ Computer Science 8:e1185 https://doi.org/10.7717/peerj-cs.1185

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.↩︎

The encrypted notes app market is experiencing robust growth, driven by increasing concerns over data privacy and security. The market, estimated at $2 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033, reaching an estimated $7 billion by 2033. This expansion is fueled by several key factors. The rising adoption of cloud-based services, coupled with the increasing sophistication of cyber threats, is pushing individuals and businesses to seek secure solutions for storing sensitive information. Furthermore, stringent data privacy regulations globally are incentivizing the development and adoption of encrypted note-taking applications that comply with these rules. The market's segmentation includes various features like end-to-end encryption, cross-platform compatibility, and collaboration tools, catering to different user needs. Competition among established players like Evernote and Dropbox alongside innovative startups like Standard Notes and NoteCrypt is driving innovation and improving product offerings. The market’s growth isn't without challenges. The complexity of encryption technology can create a barrier to entry for less tech-savvy users. Price sensitivity and the availability of free or basic alternatives remain factors affecting market penetration. However, ongoing advancements in user interface design and the incorporation of features like biometric authentication are mitigating these concerns. Future growth will likely be propelled by increased integration with other productivity tools, the expansion of AI-powered features (like enhanced search and organization), and the continued focus on improving user experience and accessibility. Geographic expansion into emerging markets with growing digital literacy will also significantly contribute to market expansion. Companies need to focus on building robust security features, clear and intuitive user experiences, and effective marketing strategies to capture a larger market share in this rapidly evolving landscape.

According to Cognitive Market Research, the global Mobile User Authentication market size is USD 1952.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 23.50% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 780.88 million in 2024 and will grow at a compound annual growth rate (CAGR) of 21.7% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 585.66 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 449.01 million in 2024 and will grow at a compound annual growth rate (CAGR) of 25.5% from 2024 to 2031.
Latin America had a market share for more than 5% of the global revenue with a market size of USD 97.61 million in 2024 and will grow at a compound annual growth rate (CAGR) of 22.9% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 39.04 million in 2024 and will grow at a compound annual growth rate (CAGR) of 23.2% from 2024 to 2031.
The Two-Factor Authentication held the highest Mobile User Authentication market revenue share in 2024.

Market Dynamics of Mobile User Authentication Market

Key Drivers for Mobile User Authentication Market

Surging Mobile Device Usage to Increase the Demand Globally

The pervasive use of smartphones and tablets has caused an urgent demand for robust authentication methods to safeguard non-public and expert facts. With those gadgets serving as relevant repositories for sensitive statistics, secure admission is vital to save you from unauthorized breaches. Biometric authentication, which includes fingerprint and facial reputation, has gained prominence for its reliability and comfort. Additionally, multi-aspect authentication (MFA) combining passwords with biometrics or OTP (one-time password) verification offers enhanced protection layers. As cellular gadgets continue to conform and combine deeper into daily activities, the development and adoption of stringent authentication protocols stay paramount to mitigate risks associated with data breaches and identity robbery, making sure customers can expectantly navigate their digital lives.

Growing Security Threats to Propel Market Growth

The escalation of cybercrime, encompassing phishing attacks and information breaches, underscores the critical need for robust cell authentication answers among both corporations and individuals. As smartphones and tablets increasingly save and get the right of entry to sensitive facts, they become prime objectives for malicious actors in search of unauthorized access. Effective cellular authentication strategies include biometric verification (along with fingerprint or facial popularity) and multi-element authentication (MFA), combining passwords with extra security layers like OTPs (one-time passwords) or hardware tokens. These measures help give a boost to defenses against evolving cyber threats, ensuring that entry to to non-public and enterprise data stays steady. Implementing and continuously updating these authentication answers are critical steps in mitigating dangers associated with the growing sophistication of cyberattacks in the modern-day interconnected digital landscape.

Restraint Factor for the Mobile User Authentication Market

Privacy Concern to Limit the Sales

The use of biometric facts for authentication functions has sparked an ongoing debate over privacy worries. While biometrics provide comfort and strong protection, storing sensitive non-public records inclusive of fingerprints or facial features on gadgets raises apprehensions among customers. The ability dangers consist of unauthorized get admission to biometric templates and the misuse of captured facts for identification theft or surveillance purposes. Additionally, there are issues about the permanence and irreversibility of biometric statistics as soon as compromised. To address those problems, builders, and policymakers suggest for stringent security features, encryption protocols, and transparent user consent rules. Balancing the advantages of biometric authentication with safeguarding personal privacy is essential in fostering the belief and adoption of these technologies in the digital age.

Impact of Covid-19 on the Mobile User Authentication Market

The COVID-19 pand...

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Passwordless authentication is a security process which typically relies on security tokens, personal identification numbers, and an individual's unique biological characteristics such as: fingerprint recognition, iris recognition, face recognition, voice analysis, and many others. This authentication method comes without the use of passwords and has several advantages, such as improved user experience and higher security. According to Next Move Strategy Consulting, the passwordless authentication market was valued at 15.6 billion U.S. dollars in 2022 and was forecast to exceed 53 billion U.S. dollars by 2030.

Passwordless implementation

North America (NA) was the region with the highest passwordless access implementation by 2022, as opposed to Asia-Pacific (APAC). However, respondents worldwide stated that their company planned on implementing passwordless access in the next 12 to 18 months. From a country point of view, in 2021, 77 percent of respondents in India considered implementing a passwordless strategy in their company, whereas the share of respondents for Japan, Germany, and United Arab Emirates was below 40 percent.

Reasons to adopt a passwordless authentication

The most important reason companies worldwide turned to passwordless authentication methods was that they provided increased security. A passwordless authentication method also has a significant positive impact from an administrative point of view. In other words, passwordless solutions were considered easy to use and integrate within a company framework. Nevertheless, many companies do not have the necessary IT resources for this type of transition or are not yet familiar with the process.

As per Cognitive Market Research's latest published report, the Global Digital Vault market size was USD 763.81 Million in 2022 and it is forecasted to reach USD 2.86 Billion by 2030. Digital Vault Industry's Compound Annual Growth Rate will be 13.21% from 2023 to 2030. Factors Affecting Digital Vault market growth

Increasing demand for the digital vault in online transactions, documentation stored online, and safeguarding sensitive data are key factors expected to drive the growth of the digital vault market. Furthermore, the rising development of advanced technologies such as artificial intelligence, IoT, cloud computing, and network analytics is anticipated to the growth opportunities of the target market in the forecast period. However, the increasing cost of the digital vault and alternative cybersecurity solutions may hamper the growth of the digital vault market.

Increasing legislation aimed at protecting sensitive information is fueling market growth.

The regulation mandates that financial institutions safeguard their customers' information and ensure the security of operations in the industry. The regulation covers all DFS-regulated companies, including their foreign branches outside the state and abroad, and mandates that such companies assess the cybersecurity risk profiles of their organizations and adopt an all-encompassing plan that recognizes and mitigates the risk. The need for the digital vault market will be significantly fueled by the growing compliance and regulatory mandates for data security. Also, escalating need for secure repositories for storing vital data, like passwords and documents, over the long term is expected to propel the market for digital vaults. This is because more and more digitization is taking place and there is a greater demand for data privacy. Implementation and creating a strong cyberdefense system is an elementary measure for protecting infrastructure from active and passive threats. The data security systems are constructed based on government-established standards and norms. The data security framework requires blueprints and network mapping of the entire system, which can be done by evaluating security threats, identifying network assets, creating security plans and policies, educating the users, and installing and preserving security infrastructure.

Key Restraint of Market.

Cloud-based digital vaults are mostly dependent on stable internet connections, which creates issues in areas of poor connectivity.

Although cloud-based digital vaults offer a simple-to-scale and high-security data storage solution, their over-reliance on stable internet connection makes them impractical in areas where network infrastructure is unreliable or unstable. Such solutions are dependent on an uninterrupted and robust internet connection for crucial operations like data synchronization, real-time updates, and remote accessibility. This can slow down users, render them inaccessible, or in even some instances, interfere with access to vital data, slowing the uptake of cloud-based digital vaults in these regions. Inadequate internet infrastructure can interfere with operations for companies in rural or underdeveloped areas, lowering the efficiency of digital vault systems. Frequent connectivity issues may also cause security loopholes because it leaves a window open in the transmission of data, i.e., it may lead to the transfer of incomplete data or broken updates that keep the sensitive information vulnerable to threats. Thus, organizations can find it challenging to fully utilize the features of cloud-native digital vaults, which are something like scalability and seamless integration into the rest of the cloud services.

What is Digital Vault?

A digital vault is an online security vault, which is used to keep digital assets safe and secure critical information. A digital vault is various assets including digital files photos, videos, online login information, and banking information that can be securely stored in the digital vault.