A method of comprehensive passive email address collection and validation automation system using open-source intelligence

Abstract

A method of comprehensive passive email address collection and validation automation system using open-source intelligence comprise the following working steps; input collection and validation step, data processing step, email pattern estimation step, email validation step, data integration and output step.

Description

[0001] DESCRIPTION

[0002] A METHOD OF COMPREHENSIVE PASSIVE EMAIL ADDRESS COLLECTION AND VALIDATION AUTOMATION SYSTEM USING OPEN- SOURCE INTELLIGENCE

[0003] TECHNICAL FIELD OF THE INVENTION

[0004] The invention relates to a method of comprehensive passive email address collection and validation automation system using open-source intelligence.

[0005] PRIOR ART

[0006] In the state of the art, email collection and validation in Open-Source Intelligence (OSINT) relied heavily on basic tools and manual workflows. Existing OSINT tools primarily focused on gathering email addresses from public sources, such as websites and search engines, but lacked comprehensive automation. Users were required to manually verify and organize the collected data, which often involved checking email syntax, validating domain existence, and ensuring the relevance of information through separate, standalone tools. This fragmented approach not only increased the workload but also introduced inefficiencies, errors, and inconsistencies in the process.

[0007] In fields like investigative journalism, where accurate and timely information is critical, these limitations hindered journalists’ ability to uncover networks and validate sources effectively. Similarly, in law enforcement and government intelligence operations, manual processes created bottlenecks when analyzing large datasets to track criminal activity, cyber threats, or uncovering fraud schemes.

[0008] For military and national security applications, reliance on outdated manual workflows slowed intelligence gathering, particularly in monitoring adversarial activities through public email data. Penetration testers and cybersecurity analysts, tasked with assessing organizational security postures, faced challenges when validating large volumes of emails for threat profiling, increasing the risk of overlooking critical vulnerabilities. In the domain of threat intelligence and strategic decision -making, businesses and governments struggled to scale OSINT operations when handling massive datasets. The inefficiencies of manual workflows made it difficult to analyze real-time threats or make informed decisions promptly. Similarly, for marketing and corporate intelligence, these limitations delayed efforts to collect, validate, and organize prospect email lists, impacting lead generation and audience targeting.

[0009] In the state of the art, there is a significant gap in research and solutions in the field of passive email address collection and validation automation system using open-source intelligence. Patents and products in the state of the art are listed below:

[0010] 1. Patent US20110258187A1 - Relevance-Based OSINT Collection: This patent outlines a system that collects publicly available OSINT data using Natural Language Processing (NLP) and relevance scoring. It prioritizes information based on predefined criteria, aggregating relevance scores to display the most pertinent data to users. While it focuses on data prioritization and relevance, it does not address automation in email validation or provide comprehensive insights into email data. The patent remains limited to general OSINT collection rather than email-specific workflows.

[0011] 2. Patent US10333976B1 - Open Source Intelligence Deceptions: This patent details a system that creates and plants deceptive OSINT data to detect attackers. It involves automating the generation of fake credentials or files and planting them in public resources to lure attackers. Although it automates certain aspects of OSINT, its focus is on deception-based cybersecurity techniques rather than authentic email collection or validation.

[0012] 3. OSINT Email Tools: Several existing tools, such as Harvester or Email Hunter, primarily facilitate email collection from public sources. These tools lack integration with validation processes or automation for handling large datasets, often requiring manual effort to verify and analyze the collected data. Additionally, their scope is generally limited to simple collection tasks without providing detailed insights or scalability for enterprise use. In the state of the art, email collection and validation in OSINT relied on basic tools and manual workflows, requiring users to manually verify, organize, and validate data using standalone tools. These fragmented processes were labor-intensive, error-prone, and inefficient, particularly for large datasets or time-sensitive tasks. Existing solutions lacked automation and integration, limiting scalability and reliability.

[0013] Moreover, technical problems in the state of the artare as follows:

[0014] Inefficiency and labor-intensive nature of manual email collection and validation processes.

[0015] Challenges in handling and analyzing large datasets accurately and quickly. - Lack of integrated tools capable of providing detailed insights without human intervention.

[0016] The invention brings technical solves to the technical problems in the state of the artstated above.

[0017] BRIEF DESCRIPTION OF THE INVENTION

[0018] The aim of the invention is to revolutionize email collection and validation processes within OSINT by introducing a fully automated and efficient approach. It eliminates the need for manual intervention, providing a streamlined and scalable solution for gathering and analyzing email data. The invention empowers users with rapid, accurate results, catering to fields like cybersecurity, marketing, and business strategy.

[0019] The invention addresses the challenge of automating data analysis through the development of novel algorithms. These algorithms are crafted to optimize efficiency, ensure precise validation, and eliminate redundancies in email datasets. By tackling the complexities of large-scale data handling, the invention introduces a robust, automated framework that simplifies processes traditionally reliant on extensive manual effort. The advantage of the invention is as follows:

[0020] 1. Automation of Processes: The invention automates the entire flow management of email collection and validation, drastically reducing manual effort and human error. This automation saves time and resources, making it far more efficient than previous methods.

[0021] 2. Scalability: Unlike existing tools, the system is capable of handling large datasets efficiently, making it suitable for enterprise-level tasks or projects requiring quick results at scale.

[0022] 3. Comprehensive Integration: By combining email collection and validation into a single system, the invention eliminates the need for multiple tools, offering users a seamless and reliable solution.

[0023] 4. Enhanced Efficiency: The invention processes data faster and with greater accuracy, enabling users to complete tasks in significantly less time compared to manual or semi-automated methods.

[0024] 5. User-Friendly Reporting: It provides detailed and well -structured outputs that can be easily understood by both technical and non-technical users. This simplifies decision-making and allows for broader application.

[0025] 6. Versatility: The system is adaptable to various use cases, including cybersecurity, marketing analysis, and strategic planning, offering value across different sectors.

[0026] One of the main aspect of the invention is to its automation and innovative algorithms, which transform old manual and fragmented processes into a unified, efficient, and scalable solution. This ability to collect, validate, and analyze email data in a fully automated manner, combined with its capability to handle large datasets swiftly, sets it apart from existing solutions. This innovation not only accelerates process flow but also enables organizations to make strategic decisions based on fast and reliable data, redefining standards in OSINT tools.

[0027] The contribution of the invention to the sector is as follows:

[0028] 1. Fast and Detailed Analysis: The invention enables rapid and detailed email data analysis, providing users with actionable insights in a fraction of the time required by traditional methods. By automating processes like validation and pattern estimation, it ensures comprehensive and accurate results, significantly reducing the time spent on manual tasks. Automation: By eliminating manual steps, the invention revolutionizes workflows, making the process efficient and error-free.

[0029] Strategic Decision Support: The system helps organizations leverage validated email data to plan marketing strategies or achieve other business goals effectively.

[0030] Scalability: The invention is designed to handle large-scale datasets, making it suitable for both small-scale OSINT tasks and enterprise-level applications. Sector-Specific Applications: It contributes to improving cybersecurity practices and opens avenues for new business opportunities by enhancing data-driven strategies.

[0031] Reduction of Human Risk: Manual workflows are inherently prone to errors, inconsistencies, and biases. The invention minimizes these risks by automating critical processes such as email validation and breach data crossreferencing. It ensures accuracy and reliability, reducing dependency on human expertise and lowering the risk of oversight or errors in time-sensitive tasks.

[0032] Time Efficiency: The invention significantly reduces the time required for email collection and validation compared to traditional methods. Manual workflows, which can take hours or even days for large datasets, are replaced with an automated system capable of processing data in a fraction of the time. By streamlining processes like data extraction, validation, and reporting, the invention accelerates OSINT tasks, making it especially valuable for timesensitive applications such as cybersecurity incident response, investigative journalism, and law enforcement operations.

[0033] Cost Efficiency: By automating labor-intensive processes, the invention minimizes the need for human intervention, thereby reducing operational costs. Organizations can reallocate resources previously spent on manual validation or standalone tools to other critical activities. Furthermore, the integration of multiple functionalities into a single system eliminates the need for separate tools, providing significant cost savings for both small-scale users and enterprise-level operations. Description of the Figures Figure 1. System diagram of the method of comprehensive passive email address collection and validation automation system using open-source intelligence.

[0034] Figure 2. Functional diagram of the method of comprehensive passive email address collection and validation automation system using open-source intelligence.

[0035] DETAILED DESCRIPTION OF THE INVENTION

[0036] The invention revolutionizes OSINT by automating email collection and validation into a unified, efficient, and scalable system. It eliminates manual effort, reduces errors, and accelerates processing, making it ideal for applications such as investigative journalism, cybersecurity, law enforcement, and threat intelligence.

[0037] The invention introduces a technically advanced method to automate email collection and validation, specifically designed for Open-Source Intelligence (OSINT) applications. The invention integrates advanced algorithms and processes such as web scraping, DNS / MX record lookups, and probabilistic email pattern estimation into a unified workflow. By leveraging API integrations and natural language processing (NLP), it provides a robust framework for analyzing email metadata and identifying valid email addresses across public data sources. The automated nature of the invention minimizes human input, improves data accuracy, and enables the efficient processing of large datasets, making it scalable for high-volume or time-sensitive applications. Its modular architecture supports integration with additional databases and tools, offering flexibility for future enhancements.

[0038] The invention is highly versatile and has significant applications across multiple domains, including:

[0039] Cybersecurity: Enables professionals to validate email addresses for threat intelligence, phishing detection, and incident response, improving the reliability of email-based threat analysis.

[0040] Law Enforcement and Government Intelligence: Assists in tracking criminal activities, fraud investigations, and uncovering adversarial networks through accurate email validation and metadata analysis. Investigative Journalism: Allows journalists to efficiently verify sources and trace digital footprints, ensuring credibility and accuracy in reporting.

[0041] - Military and National Security: Supports intelligence gathering by automating the validation of adversarial communication networks

[0042] - Marketing and Corporate Intelligence: Streamlines lead generation processes by ensuring clean and validated email datasets, improving targeting accuracy.

[0043] - Penetration Testing and Red Team Operations: Automates the identification and validation of target email accounts during security assessments.

[0044] Academic and Technical Research: Provides researchers with a reliable and scalable tool for analyzing large email datasets in studies related to cybersecurity, social networks, and OSINT methodologies.

[0045] By offering a reliable, scalable, and automated solution, the invention significantly improves workflows, reduces errors, and accelerates processes across a diverse range of professional and research-oriented applications.

[0046] The technical solves which the invention brings to the technical problems in the state of the art are as follows:

[0047] 1. Automated algorithms reduce manual effort and human error in email validation and collection.

[0048] 2. The system is scalable and capable of processing large datasets efficiently, ensuring accuracy and speed.

[0049] 3. Integration of data analysis and validation into a single automated workflow enhances usability and reliability.

[0050] The invention introduces a fully automated method for email analysis and validation in OSINT tools. The method streamlines the process by:

[0051] 1. Automatically gathering, validating, and analyzing email addresses.

[0052] 2. Integrating with multiple external databases and public APIs (e.g., WHOIS, breach data, DNS, MX).

[0053] 3. Applying advanced algorithms such as web scraping, pattern estimation, and NLP to provide accurate, scalable, and efficient results. Comprehensive Analysis of the method steps:

[0054] Step 1: Input Collection and Validation.

[0055] Data Input

[0056] 1. File Uploads:

[0057] - Users upload datasets in standard formats like CSV or TXT files. Example: A CSV file might contain columns such as " Name," " Email," and " Domain."

[0058] - Files are parsed to extract the required data (e.g., email addresses). 2. Search Engine Queries:

[0059] - Users provide queries targeting email-related information, such as: ‘site: example. com’ to find email addresses associated with a domain. Advanced queries can be constructed using Boolean operators or search modifiers to refine results.

[0060] Automated Validation

[0061] 1. File Validation:

[0062] Checks if the file format is supported (e.g., CSV, TXT).

[0063] Verifies data structure using libraries like Python’s pandas to ensure the presence of required fields (e.g., email column).

[0064] 2. Query Validation:

[0065] - Ensures search queries follow proper syntax.

[0066] - For instance, checks for unsupported characters or invalid operators.

[0067] Step 2: Data Processing.

[0068] Data Extraction

[0069] 1. Web Scraping:

[0070] - How It Works:

[0071] - Uses tools like Selenium or BeautifulSoup to simulate user interactions or parse static HTML pages.

[0072] Scrapes email addresses and metadata from publicly accessible websites and search engine results. - Ensures compliance with legal and ethical guidelines by scraping only publicly available data.

[0073] 2. External Databases:

[0074] Queries databases like WHOIS, PGP repositories, and breach records via APIs to retrieve supplemental email metadata.

[0075] Content Analysis

[0076] 1. Natural Language Processing (NLP):

[0077] Analyzes metadata for symbolic entities like names, organizations, or affiliations.

[0078] - Uses pre-trained models like SpaCy or NLTK for entity recognition and extraction.

[0079] Example Workflow:

[0080] Input: john.doe@example.com

[0081] Output: Extracted entity: John Doe, domain: example.com, organization: Example Corp.

[0082] Step 3: Email Pattern Estimation.

[0083] Automated Pattern Detection

[0084] 1. How Patterns are Identified:

[0085] - Extracts known email formats from datasets.

[0086] Common patterns include:

[0087] firstname.lastname@domain.com

[0088] f.lastname@domain.com

[0089] firstname@domain.com

[0090] 2. Regex-Based Pattern Detection:

[0091] - Uses regular expressions (regex) to identify and extract email patterns. - Example Regex: match standard email format, [a-zA-Z0-9._%+-]+@[a- zA-Z0-9.-]+\.[a-zA-Z]{2,}

[0092] 3. Probabilistic Algorithms:

[0093] Generates potential email variations based on observed patterns.

[0094] - Example:

[0095] Input: Known email patterns for example.com Output: Generated emails like j.doe@example.com, john.d@example.com.

[0096] Step 4: Email Validation.

[0097] Automated Verification:

[0098] 1. DNS Lookups:

[0099] Queries the DNS system to check if an email domain has valid MX (Mail Exchange) records.

[0100] Cross-Referencing:

[0101] 1. Public Breach Databases:

[0102] Queries repositories like " Have I Been Pwned" to check if an email has been compromised.

[0103] 2. PGP Key Repositories:

[0104] Searches for public PGP keys to validate email ownership and metadata.

[0105] Step 5: Data Integration and Output.

[0106] 1. Seamless Integration:

[0107] Combines and integrates results from multiple external sources and APIs. Stores processed data in an Internal Results Database for future reference.

[0108] 2. Automated Reporting:

[0109] Generates visualized reports and dashboards for users and admins.

[0110] - Logs all processes in a Log Database for auditing and debugging purposes.

[0111] 3. Technical Terms and Definitions:

[0112] - Web Scraping: The process of extracting data from websites using automated scripts or tools (e.g., Selenium, BeautifulSoup).

[0113] - Natural Language Processing (NLP): A field of AI focused on analyzing and understanding human language.

[0114] - MX Records: DNS records that specify the mail servers for a domain. - Probabilistic Algorithms: Algorithms that predict outcomes based on statistical patterns and probabilities. Search Engine Queries: Automated or manual searches conducted on search engines (e.g., Google, Bing, Yahoo) to gather publicly available information.

[0115] - WHOIS Database: A repository containing domain registration details, including ownership, creation dates, and expiration dates.

[0116] - Breach Databases: Collections of compromised email addresses and associated information from security breaches (e.g., " Have I Been Pwned").

[0117] - DNS Lookups: Queries made to the Domain Name System (DNS) to retrieve domain-related records.

[0118] - Public Breach Databases: Openly accessible repositories that list data breaches, often containing email addresses, passwords, and other sensitive information.

[0119] - Public PGP Key Repositories: Databases containing cryptographic keys (PGP: Pretty Good Privacy) linked to email addresses.

[0120] APIs (Application Programming Interfaces): Interfaces provided by external services to programmatically access data or functionality.

[0121] Internal Results Database: A centralized storage system for processed and validated email data, including patterns, results, and metadata.

[0122] - Log Database: A repository for recording system events, such as user actions, errors, and process outcomes.

[0123] Auditing: Track user and system activities for compliance or oversight. - Debugging: Diagnose and resolve issues by analyzing logged errors and process logs.

[0124] - Regular Expressions (regex): A sequence of characters that defines a search pattern for text matching and extraction.

[0125] SpaCy: A robust library for NLP tasks such as entity recognition, tokenization, and text classification.

[0126] - NLTK (Natural Language Toolkit): A Python library for text processing tasks like tokenization, stemming, and sentiment analysis.

[0127] Selenium: Automates browser interactions, enabling dynamic content scraping from JavaScript-heavy websites. - BeautifulSoup: Parses static HTML content for structured data extraction. - Pandas: A data manipulation library in Python, used for organizing and cleaning datasets, including email lists in formats like CSV or Excel.

[0128] The innovations brought by the invention compared to the traditional methods in the state of the art are shown in the Table 1.

[0129] Table 1: The innovations brought by the invention compared to the traditional methods.

[0130] Feature Traditional Method The Invention

[0131] Input Validation Manually validated by users Automated validation of files and queries

[0132] Data Extraction Requires manual searches Unified automated web across platforms like search scraping and API integrations engines, websites or forums collect data from multiple sources simultaneously.

[0133] Content Users manually review email Uses NLP to automatically Analysis content, often missing key extract and analyze symbolic symbolic data like names or data, keywords, and affiliations affiliations from email metadata

[0134] Email Pattern Users manually observe and Automated recognition of email Estimation guess email patterns using structures using probabilistic trial and error algorithms, generating likely variations for analysis Verification DNS and MX lookups are Fully automated validation performed by hand, requiring using DNS or MX lookups, and users to interact with DNS breach database checks tools or mail servers

[0135] Reporting No reports, users try to Automated reports and compile results by themselves, dashboards summarize results which can be time-consuming in easy-understand format,

[0136]

[0137] and inconsistent ready for export

[0138] The system diagram of the method of the invention presents a streamlined process, focusing on core functionalities such as data collection, validation, cleaning, and reporting (Figure 1).

[0139] 1. Input from User / Admin: 1. Users provide email data via file uploads, queries, or direct input.

[0140] 2. Admins oversee the process, ensuring smooth operations and accessing higher-level functions like report generation.

[0141] 2. Data Collection

[0142] Aggregates email data from:

[0143] API and Breach Database: Collects compromised email details from external repositories.

[0144] - MX and DNS Records: Validates email domains by retrieving DNS / MX records for associated domains.

[0145] - Ensures data completeness by combining inputs from multiple sources.

[0146] 3. Email Validation

[0147] Validates email structure and existence through:

[0148] Syntax Checks: Ensures email addresses adhere to standard formats.

[0149] MX Verification: Confirms that the domain has a functional mail server and accepts incoming messages.

[0150] 4. Data Cleaning and Classification

[0151] - Data Cleaning:

[0152] Identifies and removes duplicates from collected data.

[0153] - Resolves inconsistencies in formats or missing information.

[0154] Classification:

[0155] Segments emails into categories (e.g., valid, invalid, compromised). Adds metadata such as breach history or PGP association for actionable insights.

[0156] 5. Internal Results Database

[0157] Stores processed data for:

[0158] - Easy access for report generation.

[0159] - Historical tracking and refined analysis.

[0160] 6. Output

[0161] - Detailed Reports: Provides users with actionable insights, summaries, and visualized data outputs.

[0162] Enables exporting for external use.

[0163] The process starts with input from the User Panel or Admin Panel, where users upload email datasets through files or provide individual email addresses. Admins manage configurations, oversee system operations, and access validation results. The system moves into the Data Collection phase, which integrates multiple data sources to gather email information:

[0164] - Web Scraping: Automates the collection of email data from publicly available websites and search engine results, capturing addresses and metadata.

[0165] - Public Databases:

[0166] API Integrations: Queries external APIs to fetch breach-related information.

[0167] - DNS / MX Records: Validates the domain-level configuration of email addresses to ensure the domain exists and can receive emails.

[0168] The collected data flows into the Email Validation stage, where the system verifies email syntax, ensures domains are configured correctly (via DNS / MX records. Once validated, the system processes the data further in the Data Cleaning and Classification phase:

[0169] - Data Cleaning: Identifies and removes duplicate or invalid entries, ensuring a clean dataset.

[0170] Classification: Segments emails into categories (e.g., valid, invalid, compromised) and enriches them with metadata such as breach history, public key associations, or domain reliability scores.

[0171] The cleaned and categorized data is stored in the Internal Results Database, which acts as a centralized hub for validated data. From here, the system generates Detailed Reports summarizing validation outcomes, classifications, and associated metadata. These reports are accessible to users and admins via the respective panels, offering actionable insights in an intuitive format. The functional diagram of the method of the invention illustrates a modular and detailed flow of how the system automates email collection, validation, and reporting, using external and internal databases for OSINT (Figure 2).

[0172] 1. Input Layer

[0173] 1. User:

[0174] a. Users initiate the process by uploading files, entering data, or performing queries.

[0175] b. Data sources include file uploads (TXT, CSV) or search engines like Bing or Yahoo.

[0176] 2. Admin:

[0177] a. Admins have additional privileges, such as monitoring processes, managing databases, and accessing logs.

[0178] 3. Validation Process:

[0179] a. Authentication Validation: Ensures the credentials and permissions of the user are valid.

[0180] b. Upload Validation: Validates the structure and format of uploaded files, ensuring they meet the required criteria for further processing. 2. Data Processing

[0181] - The central layer where automation and analysis occur:

[0182] a. Content Analysis:

[0183] i. Analyzes text and metadata extracted from emails.

[0184] ii. Uses NLP (Natural Language Processing) to identify keywords, names, affiliations, and symbolic entities.

[0185] b. Email Address Verification:

[0186] i. Verifies email structure (syntax validation).

[0187] ii. Uses DNS and MX records to confirm the existence of the domain's mail server.

[0188] c. Pattern Estimation:

[0189] i. Uses probabilistic algorithms to predict email patterns for unknown domains (e.g., first.last@domain.com). d. PGP Key Search:

[0190] i. Searches for public PGP keys associated with email addresses in public repositories.

[0191] e. Error Handling:

[0192] i. Automatically detects and handles issues, such as failed email validations or duplicate entries.

[0193] 3. Internal Results Database

[0194] 1. Stores all validated and processed email data for future analysis and reporting.

[0195] 2. Ensures scalability and access to historical data for refined analysis. 4. Integration Layer

[0196] 1. External Databases:

[0197] a. WHOIS: Retrieves domain registration information.

[0198] b. PGP Key Database: Collects cryptographic key metadata.

[0199] c. Breach Database: Checks if the email address has been exposed in data breaches.

[0200] 2. External services and databases feed into the data processing and validation pipeline.

[0201] 5. Log Database

[0202] 1. Captures system activity, such as validation events, errors, and user actions.

[0203] 2. Provides audit trails for monitoring and debugging.

[0204] 6. Output Layer

[0205] 1. Comprehensive Reports:

[0206] a. Visualized results tailored to user needs.

[0207] b. Summarizes validated emails, patterns, and insights.

[0208] 2. Dashboard:

[0209] a. Offers an interactive interface for users and admins to explore results in real-time.

[0210] The workflow starts with input from the user panel or admin panel, where users can upload email data via files, manual queries, or search engine results. Admins oversee the process by managing system configurations, monitoring integrations, and reviewing logs. The first stage is the validation process, where the system performs authentication validation to verify user credentials and ensures the uploaded data is correctly formatted through upload validation.

[0211] After validation, the data enters the Data Collection phase, where the system aggregates email information from multiple sources:

[0212] - Web Scraping: The system crawls publicly accessible web pages and search engine results (e.g., Google, Bing) using automated scripts to collect email addresses and metadata.

[0213] - Public Databases:

[0214] - WHOIS Database: Retrieves domain registration information, such as ownership and creation dates.

[0215] - PGP Key Repositories: Searches for public encryption keys associated with the emails to validate ownership.

[0216] - Breach Databases: Cross-references email addresses with public records of data breaches to identify compromised accounts.

[0217] The collected data is sent to the Data Processing layer, where several operations occur. Content Analysis uses NLP (Natural Language Processing) to extract metadata such as names, affiliations, and symbolic keywords from email content. Email Validation confirms the correctness of email syntax, checks domain existence via DNS / MX record lookups, and verifies. If domain-level email patterns need to be analyzed, Pattern Estimation employs probabilistic algorithms to predict likely email structures (e.g., first.last@domain.com). For emails linked to PGP encryption keys, the system retrieves metadata from public repositories to verify associations. Any errors during this process, such as invalid entries or duplicates, are handled by the Error Handling module.

[0218] Processed and validated data is stored in the Internal Results Database, where it can be accessed for further analysis or reporting. This database serves as the core repository, supporting the generation of Comprehensive Reports and Dashboard Visualizations, which are accessible to both users and admins. Additionally, the Integration Layer ensures seamless interaction with external databases and APIs, enriching the dataset with contextual insights. Finally, the Log Database records all system activities, providing audit trails for monitoring, debugging, and compliance purposes.

[0219] The invention is highly applicable to the cybersecurity and digital forensics industries, particularly in the domains of email security analysis, investigating journalism, strategic-decision, and threat intelligence. The tool focuses on listing emails, identifying compromised ones, and providing detailed content analysis. It is valuable for organizations, investigators, and cybersecurity professionals to detect phishing attempts, prevent data breaches, and strengthen email communication security. Its applications include:

[0220] 1. Threat Intelligence Platforms: Detecting and analyzing compromised emails to mitigate phishing and spoofing risks.

[0221] 2. Cybersecurity Operations Centers (CSOCs): Monitoring email systems for vulnerabilities and potential breaches.

[0222] 3. Digital Forensics: Investigating email-based cybercrimes and retrieving actionable intelligence.

[0223] 4. Corporate Security Teams: Securing business communication channels and identifying high-risk emails.

[0224] 5. Investigating Journalism: Journalists working on stories involving corruption, cybercrime, or organizational misconduct.

[0225] 6. Strategic-decision making: Providing comprehensive analyses of email data to mitigate risks, enhance operational security, and shape strategic policies.

[0226] 7. Regulatory Compliance: Ensuring adherence to data protection laws by identifying compromised email addresses

[0227] 8. Small and Medium Enterprises (SMEs): Help SMEs secure email communications with minimal resources by automating threat detection and validation. Also, provide accessible and cost-effective solutions for organizations with limited IT or security expertise.

[0228] 9. Non-technical Organizations: Enable non-technical users to access intuitive dashboards and automated reports without requiring cybersecurity expertise. Provide actionable recommendations and simplified interfaces to support decision-making and email security practices. Application Format: The invention is versatile and can be implemented across various platforms to ensure wide accessibility and usability:

[0229] 1. Software-as-a-Service (SaaS):

[0230] a. A cloud-based platform enabling organizations to upload datasets or connect via APIs for email validation and analysis.

[0231] b. Offers scalability for enterprises, allowing seamless handling of large datasets without requiring additional on-premises infrastructure.

[0232] c. Provides continuous updates and maintenance, ensuring organizations always have access to the latest features and security patches.

[0233] 2. Web-Based Application:

[0234] a. An intuitive, user-friendly interface accessible directly from a web browser.

[0235] b. Designed for both technical and non-technical users to perform email validation, analysis, and reporting effortlessly.

[0236] c. Real-time results and easy accessibility make it suitable for organizations of all sizes.

[0237] d. Simplified access ensures small businesses and SMEs can use the system without technical overhead.

[0238] 3. On-Premises Deployment:

[0239] a. Ideal for organizations with strict data privacy and security requirements, such as government agencies, defense organizations, or financial institutions.

[0240] b. Deployable within an organization's internal infrastructure to ensure complete control over data.

[0241] c. Allows customization to meet specific regulatory or operational needs, ensuring compliance with data protection standards like ISO27001, GDPR, KVKK or CCPA.

[0242] 4. API Integration:

[0243] a. Seamlessly integrates with existing systems such as threat intelligence platforms, email security tools, or CRM applications. b. Enhances organizational workflows by embedding email validation and analysis features directly into current software ecosystems. c. Flexible API endpoints allow tailored use cases, such as batch validation or real-time analysis during customer onboarding.

[0244] 5. Mobile Application:

[0245] a. A lightweight, portable solution enabling users to access key features from smartphones or tablets.

[0246] b. Provides on-the-go validation and analysis, particularly useful for field operations, investigative journalism, or cybersecurity professionals requiring immediate access to results.

[0247] c. Includes push notifications for real-time alerts on email breaches, validation updates, or other critical events.

[0248] d. Ensures seamless synchronization with web and SaaS platforms, offering a consistent experience across devices.

[0249] A method of comprehensive passive email address collection and validation automation system using open-source intelligence to reduce manual effort and human error in email validation and collection comprise the following working steps;

[0250] Input collection and validation step which comprises the following;

[0251] - Data input wherein comprises the file uploads and search engine queries, Automated validation wherein comprises the file validation and query validation,

[0252] - Data processing step which comprises the following;

[0253] - Data extraction wherein comprises the web scraping and external databases:

[0254] Content analysis wherein comprises the natural language processing - Email pattern estimation step which comprises the following;

[0255] Automated pattern detection wherein comprises the how patterns are identified, regex-based pattern detection and probabilistic algorithms - Email validation step which comprise the following;

[0256] Automated verification wherein comprises the DNS lookups Cross-referencing wherein comprises the public breach databases and PGP key repositories

[0257] - Data integration and output step which comprises the following;

[0258] Seamless integration wherein combines and integrates results from multiple external sources and APIs and stores processed data in an internal results database for future reference

[0259] Automated reporting wherein generates visualized reports and dashboards for users and admins and logs all processes in a log database for auditing and debugging purposes.

[0260] A method of comprehensive passive email address collection and validation automation system using open-source intelligence comprises the following working steps;

[0261] Starting with input from the user panel or admin panel, where users upload email datasets through files or provide individual email addresses.

[0262] Admins manage configurations, oversee system operations, and access validation results wherein the system moves into the data collection phase, which integrates multiple data sources to gather email information from

[0263] - Web Scraping wherein automates the collection of email data from publicly available websites and search engine results, capturing addresses and metadata.

[0264] - Public Databases which is API Integrations wherein Queries external APIs to fetch breach-related information and DNS / MX Records wherein validates the domain-level configuration of email addresses to ensure the domain exists and can receive emails.

[0265] The collected data flows into the email validation stage, where the system verifies email syntax, ensures domains are configured correctly (via DNS / MX records. Once validated, the system processes the data further in the data cleaning and classification phase from

[0266] - Data Cleaning wherein identifies and removes duplicate or invalid entries, ensuring a clean dataset. Classification wherein segments emails into categories (e.g., valid, invalid, compromised) and enriches them with metadata such as breach history, public key associations, or domain reliability scores.

[0267] The cleaned and categorized data is stored in the Internal Results Database, which acts as a centralized hub for validated data.

[0268] - From here, the system generates Detailed Reports summarizing validation outcomes, classifications, and associated metadata wherein these reports are accessible to users and admins via the respective panels, offering actionable insights in an intuitive format.

[0269] A method of comprehensive passive email address collection and validation automation system using open-source intelligence comprises the following working steps;

[0270] Starting with input from the user panel or admin panel, where users can upload email data via files, manual queries, or search engine results and admins oversee the process by managing system configurations, monitoring integrations, and reviewing logs.

[0271] The first stage is the validation process, where the system performs authentication validation to verify user credentials and ensures the uploaded data is correctly formatted through upload validation.

[0272] After validation, the data enters the data collection phase, where the system aggregates email information from multiple sources like

[0273] - Web Scraping wherein the system crawls publicly accessible web pages and search engine results (e.g., Google, Bing) using automated scripts to collect email addresses and metadata.

[0274] - Public Databases which is WHOIS database wherein retrieves domain registration information, such as ownership and creation dates and PGP key Repositories wherein searches for public encryption keys associated with the emails to validate ownership and breach databases wherein cross-references email addresses with public records of data breaches to identify compromised accounts.

[0275] The collected data is sent to the data processing layer, where several operations occur. Content analysis uses NLP (Natural Language Processing) to extract metadata such as names, affiliations, and symbolic keywords from email content.

[0276] - Email Validation confirms the correctness of email syntax, checks domain existence via DNS / MX record lookups, and verifies.

[0277] If domain-level email patterns need to be analyzed, pattern estimation employs probabilistic algorithms to predict likely email structures (e.g., first.last@domain.com).

[0278] - For emails linked to PGP encryption keys, the system retrieves metadata from public repositories to verify associations. Any errors during this process, such as invalid entries or duplicates, are handled by the error handling module.

[0279] - Processed and validated data is stored in the internal results database, where it can be accessed for further analysis or reporting.

[0280] This database serves as the core repository, supporting the generation of comprehensive reports and dashboard visualizations, which are accessible to both users and admins and the integration layer ensures seamless interaction with external databases and APIs, enriching the dataset with contextual insights. - Finally, the log database records all system activities, providing audit trails for monitoring, debugging, and compliance purposes.

[0281] The invention is not limited to the above exemplary embodiments, and a person skilled in the art can readily put forward embodiments of the invention. These are considered within the scope of the invention as claimed by the accompanying claims.

Claims

CLAIMS1. A method of comprehensive passive email address collection and validation automation system using open-source intelligence to reduce manual effort and human error in email validation and collection, characterized in that, it comprises the following working steps;Input collection and validation step which comprise the following;- Data input wherein comprise the file uploads and search engine queries, Automated validation wherein comprise the file validation and query validation,- Data processing step which comprise the following;- Data extraction wherein comprise the web scraping and external databases:Content analysis wherein comprise the natural language processing - Email pattern estimation step which comprise the following;Automated pattern detection wherein comprise thehow patterns are identified, regex-based pattern detection and probabilistic algorithms - Email validationstep which comprise the following;Automated verification wherein comprise the DNS lookups Cross-referencing wherein comprise thepublic breach databases and PGP key repositories- Data integration and output step which comprise the following;Seamless integration wherein combines and integrates results from multiple external sources and APIs and stores processed data in an internal results database for future referenceAutomated reporting wherein generates visualized reports and dashboards for users and admins and logs all processes in a log database for auditing and debugging purposes.

2. A method of comprehensive passive email address collection and validation automation system using open-source intelligence according to claim 1, characterized in that, it comprises the following working steps;Starting with input from the user panel or admin panel, where users upload email datasets through files or provide individual email addresses.Admins manage configurations, oversee system operations, and access validation results wherein the system moves into the data collection phase, which integrates multiple data sources to gather email information from- Web Scraping wherein automates the collection of email data from publicly available websites and search engine results, capturing addresses and metadata.- Public Databases which is API Integrations wherein Queries external APIs to fetch breach-related information and DNS / MX Records wherein validates the domain-level configuration of email addresses to ensure the domain exists and can receive emails.The collected data flows into the email validation stage, where the system verifies email syntax, ensures domains are configured correctly (via DNS / MX records. Once validated, the system processes the data further in the data cleaning and classification phase from- Data Cleaning wherein identifies and removes duplicate or invalid entries, ensuring a clean dataset.Classification wherein segments emails into categories (e.g., valid, invalid, compromised) and enriches them with metadata such as breach history, public key associations, or domain reliability scores.The cleaned and categorized data is stored in the Internal Results Database, which acts as a centralized hub for validated data.- From here, the system generates Detailed Reports summarizing validation outcomes, classifications, and associated metadata wherein these reports are accessible to users and admins via the respective panels, offering actionable insights in an intuitive format.

3. A method of comprehensive passive email address collection and validation automation system using open-source intelligence according to claim 1, characterized in that, it comprises the following working steps;Starting with input from the user panel or admin panel, where users can upload email data via files, manual queries, or search engine results and admins oversee the process by managing system configurations, monitoring integrations, and reviewing logs.The first stage is the validation process, where the system performs authentication validation to verify user credentials and ensures the uploaded data is correctly formatted through upload validation.After validation, the data enters the data collection phase, where the system aggregates email information from multiple sources like- Web Scraping wherein the system crawls publicly accessible web pages and search engine results (e.g., Google, Bing) using automated scripts to collect email addresses and metadata.- Public Databases which is WHOIS database wherein retrieves domain registration information, such as ownership and creation dates and PGP key Repositories wherein searches for public encryption keys associated with the emails to validate ownership and breach databases wherein cross-references email addresses with public records of data breaches to identify compromised accounts.The collected data is sent to the data processing layer, where several operations occur.Content analysis uses NLP (Natural Language Processing) to extract metadata such as names, affiliations, and symbolic keywords from email content.- Email Validation confirms the correctness of email syntax, checks domain existence via DNS / MX record lookups, and verifies.If domain-level email patterns need to be analyzed, pattern estimation employs probabilistic algorithms to predict likely email structures (e.g., first.last@domain.com).- For emails linked to PGP encryption keys, the system retrieves metadata from public repositories to verify associations. Any errors during this process, such as invalid entries or duplicates, are handled by the error handling module.- Processed and validated data is stored in the internal results database, where it can be accessed for further analysis or reporting.This database serves as the core repository, supporting the generation of comprehensive reports and dashboard visualizations, which are accessible to both users and admins and the integration layer ensures seamless interaction with external databases and APIs, enriching the dataset with contextual insights. - Finally, the log database records all system activities, providing audit trails for monitoring, debugging, and compliance purposes.

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