A global survey conducted between November 2022 and January 2023 revealed that seven in ten respondents had taken steps to protect their online identity. Those who enabled multi-factor authentication were 30 percent. Additionally, 28 percent said they changed default settings on devices. On the other hand, 30 percent said they had not done anything to protect their digital identity.

The Secure Sockets Layer (SSL) Certification market, valued at $5.12 billion in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 27.17% from 2025 to 2033. This surge is driven primarily by the increasing need for secure online transactions and data protection across various sectors, including e-commerce, finance, and healthcare. The rising adoption of cloud computing and the Internet of Things (IoT) further fuels market expansion, as these technologies necessitate robust security measures. Domain validation, a foundational SSL type, constitutes a significant portion of the market, while extended validation, offering the highest level of trust, commands a premium price point and contributes to substantial revenue streams. Competitive landscape analysis reveals a diverse mix of established players and emerging vendors, each employing varying competitive strategies like strategic partnerships, technological innovation, and aggressive pricing to secure market share. The market shows strong regional variations, with North America and Europe holding substantial shares, owing to high technological adoption and stringent data privacy regulations. However, the Asia-Pacific region is poised for significant growth, fueled by rapid digitalization and expansion of internet penetration. Market restraints include the rising sophistication of cyberattacks, requiring continuous adaptation of SSL certification technologies to counter emerging threats. The complexity of implementation and management of SSL certificates for large organizations can also pose a challenge, leading to adoption barriers. However, the increasing awareness of data security breaches and associated financial and reputational risks is expected to offset these challenges. The market is segmented by validation type (Domain, Organization, Extended Validation) and geography, allowing for a granular understanding of growth opportunities within specific niches. Future market trajectories will likely be shaped by advancements in encryption technologies, the rise of quantum computing, and the evolving regulatory landscape around data security. The continuous need for enhanced security protocols within the digital economy ensures a sustainable growth outlook for the SSL certification market over the forecast period.

Code:

• Packet_Features_Generator.py & Features.py
  • To run this code:
    • pkt_features.py [-h] -i TXTFILE [-x X] [-y Y] [-z Z] [-ml] [-s S] -j
    • -h, --help show this help message and exit
      -i TXTFILE input text file
      -x X Add first X number of total packets as features.
      -y Y Add first Y number of negative packets as features.
      -z Z Add first Z number of positive packets as features.
      -ml Output to text file all websites in the format of websiteNumber1,feature1,feature2,...
      -s S Generate samples using size s.
      -j
  • Purpose:
    • Turns a text file containing lists of incomeing and outgoing network packet sizes into separate website objects with associative features.
    • Uses Features.py to calcualte the features.
• startMachineLearning.sh & machineLearning.py
  • To run this code:
    • bash startMachineLearning.sh
    • This code then runs machineLearning.py in a tmux session with the nessisary file paths and flags
    • Options (to be edited within this file):
      • --evaluate-only to test 5 fold cross validation accuracy
      • --test-scaling-normalization to test 6 different combinations of scalers and normalizers
        • Note: once the best combination is determined, it should be added to the data_preprocessing function in machineLearning.py for future use
      • --grid-search to test the best grid search hyperparameters
        - note: the possible hyperparameters must be added to train_model under 'if not evaluateOnly:'
        - once best hyperparameters are determined, add them to train_model under 'if evaluateOnly:'
  • Purpose:
    • Using the .ml file generated by Packet_Features_Generator.py & Features.py, this program trains a RandomForest Classifier on the provided data and provides results using cross validation. These results include the best scaling and normailzation options for each data set as well as the best grid search hyperparameters based on the provided ranges.

Data

• Encrypted network traffic was collected on an isolated computer visiting different Wikipedia and New York Times articles, different Google search queres (collected in the form of their autocomplete results and their results page), and different actions taken on a Virtual Reality head set.
• Data for this experiment was stored and analyzed in the form of a txt file for each experiment which contains:
  • First number is a classification number to denote what website, query, or vr action is taking place.
  • The remaining numbers in each line denote:
    • The size of a packet,
    • and the direction it is traveling.
  • negative numbers denote incoming packets
  • positive numbers denote outgoing packets

Email Verification Market size was valued at USD 5.243 Billion in 2023 and is projected to reach USD 9.849 Billion by 2031, growing at a CAGR of 8.2% during the forecast period 2024-2031.

Email Verification Market: Definition/ Overview

Email verification is a crucial process that confirms the existence, authenticity, and deliverability of an email address. It involves procedures such as syntax validation, domain validation, and mailbox validation. Email verification can be automated or manual and is used in various applications such as email marketing, account registration, customer communication, and fraud prevention.

As technology advances, email verification processes become more sophisticated, leading to higher accuracy in identifying valid email addresses and reducing false positives. AI-powered algorithms can analyze patterns and behaviors to enhance email verification processes, identifying anomalies and potential fraud more effectively.

Future email verification methods may prioritize data privacy and security, prioritizing methods that protect user privacy while ensuring verification accuracy. Blockchain technology offers potential for secure and decentralized email verification systems, reducing reliance on centralized authorities and enhancing trust in the verification process.

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.↩︎

Secure Sockets Layer Certification Market Outlook

The global Secure Sockets Layer (SSL) certification market size was valued at approximately USD 1.2 billion in 2023 and is projected to grow to USD 2.8 billion by 2032, driven by a compound annual growth rate (CAGR) of 9.5%. The primary growth factors contributing to this market expansion include the increasing need for secure online transactions, rising cyber threats, and stringent regulatory requirements for data protection. Additionally, the proliferation of e-commerce and online banking services necessitates the deployment of robust security measures such as SSL certification, further bolstering market growth.

One of the significant factors driving the growth of the SSL certification market is the escalating incidences of cyber-attacks and data breaches. With the digital transformation of businesses and the global shift towards cloud-based solutions, organizations are increasingly vulnerable to cyber threats. SSL certificates play a crucial role in encrypting data and ensuring secure communication between web servers and browsers, thereby mitigating the risk of data breaches and enhancing user trust. Additionally, the rising awareness among consumers regarding online security and privacy has led to a higher demand for SSL certifications, further propelling market growth.

Another critical growth factor is the stringent regulatory landscape for data protection and privacy. Various governments and regulatory bodies across the globe have implemented rigorous data protection laws and standards, such as the General Data Protection Regulation (GDPR) in Europe and the California Consumer Privacy Act (CCPA) in the United States. These regulations mandate the use of secure communication channels and data encryption to protect sensitive information. Consequently, organizations are compelled to adopt SSL certificates to comply with these regulations, thereby driving the market growth.

The exponential growth of e-commerce and online banking services has significantly contributed to the demand for SSL certifications. With the increasing number of online transactions and digital payments, ensuring secure data transmission and protecting customer information has become paramount for businesses. SSL certificates provide the necessary encryption to safeguard sensitive data, thereby fostering consumer trust and confidence in online platforms. This, in turn, has led to a substantial increase in the adoption of SSL certifications across various industries, including retail, BFSI, and IT and telecommunications.

Regionally, North America held the largest market share for SSL certifications in 2023, driven by the high adoption rate of advanced technologies, stringent regulatory requirements, and the presence of major market players. Europe follows closely due to the implementation of GDPR and the growing emphasis on data security and privacy. The Asia Pacific region is expected to witness significant growth during the forecast period, attributed to the rapid digital transformation, increasing internet penetration, and rising awareness about online security among consumers and businesses. Latin America and the Middle East & Africa regions are also anticipated to experience moderate growth, driven by the expanding e-commerce sector and the need for enhanced cybersecurity measures.

Type Analysis

The SSL certification market can be segmented by type into Domain Validated (DV), Organization Validated (OV), and Extended Validation (EV). Domain Validated SSL certificates are primarily used by small businesses and personal websites due to their cost-effectiveness and ease of issuance. These certificates validate the domain ownership and provide basic encryption, making them suitable for non-commercial websites. The growing number of small businesses and personal blogs is driving the demand for DV SSL certificates, thereby contributing to market growth.

Organization Validated SSL certificates, on the other hand, offer a higher level of security by verifying the organization's identity in addition to the domain validation. This type of SSL certificate is widely used by medium to large-sized businesses and e-commerce platforms that require a higher level of trust and security. The increasing number of online transactions and the need for secure e-commerce platforms are driving the demand for OV SSL certificates. Additionally, these certificates display the organization's name in the certificate details, enhancing customer trust and confidence in the website.

Extended Validation SSL certific

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

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

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

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

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

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

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

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

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

Online health communities (OHCs) have become more important to people’s daily lives on the foundation of the voluntary sharing of electronic health records (EHRs). However, no in-depth investigation has been conducted concerning the influence of the perceptions of privacy protection among patients on their willingness to share EHRs. To fill the knowledge gap, by combining and modifying the theory of planned behavior (TPB) and the health belief model in the context of the privacy protection models implemented by OHCs, an empirical research method using a questionnaire approach is conducted to validate the hypotheses. The results indicate that the more positive a patient’s attitude toward medical information sharing behavior is, the higher that patient’s level of perceived behavioral control; in addition, the greater the social rewards obtained from this process, the more willing the patient is to share his or her EHRs after privacy protection measures are implemented by OHCs. Meanwhile, the effects of past positive experiences and disease severity have also been tested. The findings of this study can be used to promote patients’ full participation in OHCs from a privacy perspective and offer theoretical and practical suggestions to promote the development of OHCs.

The global facial authentication systems market is experiencing robust growth, driven by increasing demand for secure and convenient authentication methods across various sectors. The market's expansion is fueled by the rising adoption of biometric technologies in financial services, online membership verification, ride-sharing platforms, and online education. Governments and businesses are increasingly prioritizing robust security measures, leading to a significant surge in the deployment of facial recognition systems for access control, fraud prevention, and identity verification. Furthermore, advancements in artificial intelligence (AI) and machine learning (ML) are enhancing the accuracy and efficiency of facial authentication systems, making them more reliable and user-friendly. The integration of facial recognition technology into smartphones and other consumer electronics is also contributing to market growth, as is the increasing acceptance of biometric authentication by consumers. While data privacy and security concerns represent a significant restraint, the continuous development of robust security protocols and regulatory frameworks is mitigating these risks. The market is segmented by type (online authentication systems and certificate verification systems) and application (financial services, online membership verification, ride-sharing, online education, and others), offering diverse opportunities for growth. Considering the provided data points to a period from 2019 to 2033, a logical projection is that the CAGR (Compound Annual Growth Rate), while not specified, likely falls within a range of 15-20%, reflecting the rapid technological advancements and increasing adoption. This estimate accounts for fluctuations in individual years, given economic conditions and market trends. Geographic growth is also anticipated to be significant in the Asia-Pacific region, driven by rapid economic development and a large population base. The competitive landscape is characterized by the presence of both established players and emerging technology companies. Key players are investing heavily in research and development to enhance the accuracy, speed, and security of their facial authentication systems. Strategic partnerships and mergers and acquisitions are also shaping the market dynamics. Future market growth will depend on the continued improvement of technology, the addressing of privacy concerns, and the increasing integration of facial authentication into everyday applications. The ongoing need for secure and user-friendly identity verification solutions in various sectors suggests that the facial authentication systems market will maintain a strong trajectory of growth throughout the forecast period. Considering the global nature of the market and varying economic conditions, regional growth may vary slightly, with North America and Asia Pacific expected to lead the way.

The online security vault market is experiencing robust growth, driven by the increasing need for secure data storage and access management across various sectors. The rising adoption of cloud-based solutions, coupled with stringent government regulations regarding data privacy (like GDPR and CCPA), are key catalysts. We estimate the market size in 2025 to be approximately $15 billion, based on observed growth trends in related cybersecurity sectors and considering the expanding applications across BFSI, government, IT & Telecom, and other industries. The market's Compound Annual Growth Rate (CAGR) is projected to be around 12% from 2025 to 2033, indicating substantial future expansion. This growth is fueled by the increasing sophistication of cyber threats and the consequent demand for robust, secure solutions. Key trends include the integration of AI and machine learning for enhanced threat detection and the growing popularity of multi-factor authentication for improved access control. However, challenges remain, including the high initial investment costs for implementing these solutions and the ongoing need for skilled cybersecurity professionals to manage these systems effectively. The market segmentation reveals a strong preference for cloud-based solutions, which offer scalability and cost-effectiveness. The BFSI and government sectors are significant contributors to market revenue due to their stringent security requirements and large data volumes. Competitive landscape analysis reveals that established players like CyberArk, IBM, and Microsoft are leading the market, but several niche players are also emerging, offering specialized solutions. Geographical analysis suggests North America and Europe currently hold the largest market shares, but the Asia-Pacific region is projected to witness significant growth in the coming years, driven by rising digitalization and increasing cybersecurity awareness. The forecast period (2025-2033) promises continued expansion, with the market poised to benefit from technological advancements and growing organizational investment in cybersecurity. Specific regional growth will depend on factors like digital infrastructure development and regulatory landscape changes.

As of February 2025, **** percent of the population in Burundi did not have internet access. Chad followed, with around **** percent reportedly being offline. North Korea ranked first with an internet penetration of nearly **** percent, as the internet remains blocked for its citizens. Global internet freedom and access The degree of internet freedom varies drastically across countries, with some providing open access and others imposing significant restrictions. In 2024, Iceland was leading worldwide in terms of internet freedom, while Myanmar and China ranked the lowest in the world, with strict government surveillance and censorship. The struggle with limited internet access is particularly evident in Africa, which had the lowest internet penetration rates as of 2024. Global privacy concerns The limitations and restrictions to internet access around the world prove that internet users' privacy is extremely vulnerable. And those who have access to the web are somewhat aware of that. By the third quarter of 2024, around **** percent of internet users expressed concerns about companies misusing their data. This growing awareness of privacy risks is reflected in users’ behaviour. By June 2024, ***** in ***internet users worldwide had already taken measures to protect their online privacy. Additionally, many internet users reported taking further steps, such as enabling multi-factor authentication, for stronger privacy protections.

Computer Scientists and Cyber Security Researcher Stefan Ćertić warns emerging Security as a service can be a double-edged sword. Modern asymmetric cryptography, in its essence, provide a viable solution ensuring the authenticity of a website while browsing the web and prevention of man in the middle decryption by third parity. Benefits of privacy likely inspired the Encrypted Web Initiative back in 2014 - when Google announced encrypted web communication will translate as a positive signal in search engine ranking. Following years, the SEO race made us to a point that the majority, 51.8 percent of websites use SSL. Most of internet traffic is now encrypted in transit using Transport Layer Security (TLS) – hence ISP or “a guy next-door” can’t decode your surfing habits or even passwords through ethernet or Wifi sniffing. It seems like the mission has been accomplished. So, what could go wrong? Imposed changes required a bit of technical knowledge to implement by website owners which was a perfect business opportunity adopted by a couple of startups - translated as “Let us do it for you” - Just point your Name Servers to us, and we will handle the rest. According to public data, SECaaS companies such as Cloudflare protect 12 million websites, adding approximately 20,000 new customers every day. These numbers are getting drastically higher and already occupy around 20% of Global Internet Traffic. As opposed to primary idea of Public Key Infrastructure, SECaaS had something different in core concept. Ensure you can’t reach the origin server directly. Protecting owner, not the user. Ensure encryption takes place at the edge of such service or In other words, re-encryption, a legit “Man in the middle”, so you don’t need to become a cryptography expert to follow up with Google’s initiative as website owner. With all the hats off to Google and attempts to make the web more secure and private place for end users, modern internet ended up with a few fundamental problems. Inability to validate the origin server is what is supposed to be. Inability to prevent theoretical man in the middle interceptions, theoretically putting SECaaS into privileged position to sniff traffic and execute an attack. Essentially the whole concept of cryptography got broken the very same moment millions of keys are held at the very same place – that also transits the traffic, encrypted using the very same keys. Does that mean interception of traffic no longer can be executed by a “guy next door”, your ISP, or a suspicious car parked across the street, yes! However, single email request for your data towards SECaaS providers theoretically can result in obtaining full set of data no matter where you are in the world – warns Ćertić in recent blog post published on his Information Security Consulting Blog

Transport Layer Security (TLS) Market Outlook

The global Transport Layer Security (TLS) market size was valued at approximately $1.2 billion in 2023 and is projected to reach around $2.8 billion by 2032, growing at a CAGR of 9.5% from 2024 to 2032. This robust growth trajectory can be attributed to the increasing concerns over data security, the proliferation of cyber threats, and the rapid adoption of digital transformation across various industries. TLS, as an essential technology for ensuring secure communications over computer networks, has witnessed widespread adoption, driven by the burgeoning need to protect sensitive data and maintain trust in digital transactions.

One of the primary growth factors for the TLS market is the escalating frequency and sophistication of cyber-attacks, which has made cybersecurity a top priority for enterprises worldwide. Businesses are increasingly investing in advanced security protocols like TLS to protect sensitive information from being intercepted or tampered with during transmission. The rise of e-commerce, online banking, and digital interactions has further underscored the need for robust encryption solutions, driving the demand for TLS implementations. Additionally, regulatory requirements mandating stringent data protection measures have propelled the adoption of TLS across various sectors, ensuring compliance and safeguarding against data breaches.

The expansion of cloud computing and the rise of the Internet of Things (IoT) have also significantly impacted the TLS market. As more organizations migrate their operations to the cloud, the need for secure communication protocols becomes paramount. TLS plays a crucial role in securing data transmitted between cloud applications and clients, ensuring data integrity and confidentiality. Similarly, with the proliferation of IoT devices, securing the communications between these devices and central systems is critical, further bolstering the demand for TLS solutions. The integration of TLS with emerging technologies continues to create new opportunities for market growth.

Moreover, increased consumer awareness regarding data privacy and the potential risks of data exposure has led to greater demand for secure digital interactions. End-users are now more informed and expect businesses to use the highest security measures to protect their data. This shift in consumer behavior has compelled organizations to adopt advanced security protocols like TLS to maintain customer trust and loyalty. Consequently, the market is witnessing a surge in TLS adoption across sectors such as BFSI, healthcare, retail, and more, as companies strive to align with consumer expectations and regulatory standards.

In the realm of digital security, EV SSL Certification has emerged as a pivotal component for businesses aiming to enhance their online trustworthiness. This certification, known for its rigorous validation process, provides a higher level of assurance to users by verifying the legitimacy of a website. As cyber threats continue to evolve, the demand for EV SSL Certification is on the rise, particularly among industries handling sensitive data such as finance and healthcare. By displaying a green address bar and the organization's name in the browser, EV SSL Certification not only boosts consumer confidence but also plays a crucial role in safeguarding online transactions. This added layer of security is becoming increasingly essential as businesses strive to protect their digital assets and maintain the integrity of their online presence.

Regionally, North America currently dominates the TLS market, owing to the presence of major technology companies, stringent data protection regulations, and a high level of digital infrastructure. However, Asia Pacific is expected to witness the highest growth rate during the forecast period, driven by rapid digital transformation, increasing internet penetration, and growing concerns over data security in emerging economies. Europe also presents significant growth opportunities, supported by the implementation of rigorous data privacy laws such as the General Data Protection Regulation (GDPR). These regional dynamics highlight the global nature of the TLS market and the diverse factors influencing its growth across different geographic areas.

Component Analysis

The TLS market is segmented based on components into software, hardware, and services, each playing a vital role in the deployment and maintenance of secure communication syst

AI Training Data | Annotated Checkout Flows for Retail, Restaurant, and Marketplace Websites Overview

Unlock the next generation of agentic commerce and automated shopping experiences with this comprehensive dataset of meticulously annotated checkout flows, sourced directly from leading retail, restaurant, and marketplace websites. Designed for developers, researchers, and AI labs building large language models (LLMs) and agentic systems capable of online purchasing, this dataset captures the real-world complexity of digital transactions—from cart initiation to final payment.

Key Features

Breadth of Coverage: Over 10,000 unique checkout journeys across hundreds of top e-commerce, food delivery, and service platforms, including but not limited to Walmart, Target, Kroger, Whole Foods, Uber Eats, Instacart, Shopify-powered sites, and more.

Actionable Annotation: Every flow is broken down into granular, step-by-step actions, complete with timestamped events, UI context, form field details, validation logic, and response feedback. Each step includes:

Page state (URL, DOM snapshot, and metadata)

User actions (clicks, taps, text input, dropdown selection, checkbox/radio interactions)

System responses (AJAX calls, error/success messages, cart/price updates)

Authentication and account linking steps where applicable

Payment entry (card, wallet, alternative methods)

Order review and confirmation

Multi-Vertical, Real-World Data: Flows sourced from a wide variety of verticals and real consumer environments, not just demo stores or test accounts. Includes complex cases such as multi-item carts, promo codes, loyalty integration, and split payments.

Structured for Machine Learning: Delivered in standard formats (JSONL, CSV, or your preferred schema), with every event mapped to action types, page features, and expected outcomes. Optional HAR files and raw network request logs provide an extra layer of technical fidelity for action modeling and RLHF pipelines.

Rich Context for LLMs and Agents: Every annotation includes both human-readable and model-consumable descriptions:

“What the user did” (natural language)

“What the system did in response”

“What a successful action should look like”

Error/edge case coverage (invalid forms, OOS, address/payment errors)

Privacy-Safe & Compliant: All flows are depersonalized and scrubbed of PII. Sensitive fields (like credit card numbers, user addresses, and login credentials) are replaced with realistic but synthetic data, ensuring compliance with privacy regulations.

Each flow tracks the user journey from cart to payment to confirmation, including:

Adding/removing items

Applying coupons or promo codes

Selecting shipping/delivery options

Account creation, login, or guest checkout

Inputting payment details (card, wallet, Buy Now Pay Later)

Handling validation errors or OOS scenarios

Order review and final placement

Confirmation page capture (including order summary details)

Why This Dataset?

Building LLMs, agentic shopping bots, or e-commerce automation tools demands more than just page screenshots or API logs. You need deeply contextualized, action-oriented data that reflects how real users interact with the complex, ever-changing UIs of digital commerce. Our dataset uniquely captures:

The full intent-action-outcome loop

Dynamic UI changes, modals, validation, and error handling

Nuances of cart modification, bundle pricing, delivery constraints, and multi-vendor checkouts

Mobile vs. desktop variations

Diverse merchant tech stacks (custom, Shopify, Magento, BigCommerce, native apps, etc.)

Use Cases

LLM Fine-Tuning: Teach models to reason through step-by-step transaction flows, infer next-best-actions, and generate robust, context-sensitive prompts for real-world ordering.

Agentic Shopping Bots: Train agents to navigate web/mobile checkouts autonomously, handle edge cases, and complete real purchases on behalf of users.

Action Model & RLHF Training: Provide reinforcement learning pipelines with ground truth “what happens if I do X?” data across hundreds of real merchants.

UI/UX Research & Synthetic User Studies: Identify friction points, bottlenecks, and drop-offs in modern checkout design by replaying flows and testing interventions.

Automated QA & Regression Testing: Use realistic flows as test cases for new features or third-party integrations.

What’s Included

10,000+ annotated checkout flows (retail, restaurant, marketplace)

Step-by-step event logs with metadata, DOM, and network context

Natural language explanations for each step and transition

All flows are depersonalized and privacy-compliant

Example scripts for ingesting, parsing, and analyzing the dataset

Flexible licensing for research or commercial use

Sample Categories Covered

Grocery delivery (Instacart, Walmart, Kroger, Target, etc.)

Restaurant takeout/delivery (Ub...

The online gaming security solutions market is experiencing robust growth, driven by the escalating popularity of online gaming and the increasing sophistication of cyber threats targeting this sector. The market, estimated at $2.5 billion in 2025, is projected to exhibit a Compound Annual Growth Rate (CAGR) of 15% from 2025 to 2033. This significant growth is fueled by several key factors. The rise of mobile gaming, expansion of esports, and the increasing integration of online gaming into the metaverse are all contributing to heightened security concerns. Furthermore, the growing frequency and severity of cyberattacks, including data breaches, account takeovers, and in-game cheating, are compelling game developers and publishers to invest heavily in robust security measures. The market is segmented by solution type (multi-user and single-user) and application (mobile phones, PCs, and consoles), reflecting the diverse security needs across different platforms and gaming experiences. The competitive landscape is populated by a mix of established cybersecurity vendors and specialized gaming security providers, indicating a dynamic and evolving market. The North American region currently dominates the market share, due to high online gaming penetration and stringent data privacy regulations; however, rapid growth is expected in Asia-Pacific, particularly in China and India, driven by the booming gaming industry in these regions. The continued expansion of the online gaming market, coupled with increasing awareness of data privacy and security risks, will continue to fuel market growth. The adoption of advanced security technologies, such as AI-powered threat detection, blockchain-based security, and multi-factor authentication, will play a crucial role in shaping the future of this sector. The integration of security solutions directly into game development processes, rather than as an afterthought, is also likely to gain traction. Challenges remain, including the need for constant innovation to stay ahead of evolving cyber threats and the need for cost-effective security solutions that cater to the diverse needs of various gaming platforms and player bases. The emergence of new gaming technologies and platforms will continue to introduce new security challenges and opportunities within the market.

The online transaction fraud detection market is experiencing robust growth, driven by the escalating volume of digital transactions and the increasing sophistication of fraud techniques. The market, currently valued at approximately $15 billion in 2025 (estimated based on typical market sizes for similar technology sectors), is projected to grow at a Compound Annual Growth Rate (CAGR) of 15% between 2025 and 2033. This significant expansion is fueled by several key factors: the widespread adoption of e-commerce and mobile payments; the rising prevalence of cross-border transactions, which increase vulnerability to fraud; and the growing demand for advanced analytics and AI-powered solutions to combat increasingly complex fraud schemes. Key market segments include solutions for payment processing, e-commerce, and financial institutions, each contributing significantly to the overall market size. The competitive landscape is dynamic, with established players like IBM, FIS, and FICO alongside emerging innovative companies like Feedzai and SecuroNix continuously developing and deploying new fraud detection technologies. The market’s growth, however, is tempered by factors like the high cost of implementation and maintenance of these sophisticated systems, and the ongoing challenge of staying ahead of evolving fraud tactics. The future trajectory of the online transaction fraud detection market will be shaped by several key trends. Increased investment in artificial intelligence (AI) and machine learning (ML) will lead to more accurate and real-time fraud detection. The integration of blockchain technology promises to enhance transaction security and transparency. Furthermore, the growing adoption of biometrics and behavioral analytics will further bolster fraud prevention capabilities. However, regulatory changes and evolving data privacy concerns will necessitate robust data security measures and compliance frameworks. This will present both opportunities and challenges for market players to adapt and innovate. Geographic expansion into emerging markets, particularly in Asia-Pacific and Latin America, will also contribute significantly to future growth. The continuous arms race between fraudsters and security providers ensures that this market will remain dynamic and highly competitive throughout the forecast period.