Automated Phishing Data Collection for Security Training
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing cyber security solutions fail to efficiently detect targeted cyber-attacks, particularly Business Email Compromise and phishing, leaving end users as the last line of defense, which is inadequate in preventing financial losses due to lack of effective training and awareness.
Innovation Solution
Implementing security awareness computer-based training (CBT) solutions that provide customized simulations, quizzes, and interactive courses to educate end users on recognizing phishing attempts and cyber threats, using data collection and validation services to maintain relevance with real-time phishing data and organizational-specific threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If end users are trained to identify cyber threats manually, then security awareness improves, but time consumption and training effectiveness decrease due to lack of customization
Solution Approach 1:
The system pre-collects phishing URLs, messages, and webpages from multiple sources and stores them in a database before training sessions. This preliminary data collection and organization enables rapid deployment of customized training content without time-consuming manual preparation during actual training sessions.
Solution Approach 2:
The system creates copies of real phishing attempts (URLs, messages, webpages) and stores them as training materials. These copies can be repeatedly used across multiple training sessions without requiring additional data collection, significantly reducing time consumption while maintaining training effectiveness.
2Productivity
If generic security training content is used, then training deployment is fast, but training effectiveness decreases due to lack of organizational relevance
Solution Approach 1:
The system customizes training content based on organization-specific characteristics by selecting phishing examples relevant to the organization's industry, size, and threat landscape. Each organization receives tailored training materials rather than generic content, improving effectiveness while maintaining efficient deployment through automated selection processes.
Solution Approach 2:
The system creates a universal database of phishing training materials that can be adapted to serve multiple different organizations and scenarios. The same infrastructure and data collection mechanisms serve various organizational needs, enabling both customization and efficient deployment simultaneously.
3Measurement precision
If manual collection of phishing data is performed, then data accuracy improves, but time consumption and scalability worsen
Solution Approach 1:
The system merges multiple automated data collection sources (phishing databases, message sources, webpage sources) into a unified training data repository. This combination of multiple automated sources maintains data accuracy through cross-validation while dramatically improving collection efficiency and scalability compared to manual methods.
Solution Approach 2:
The system implements automated data collection mechanisms that self-update and self-maintain the phishing training database without requiring manual intervention. The automated processes continuously collect new phishing examples, update existing data, and maintain data quality, enabling both accuracy and high productivity.
Data Source
AI summary
A method of collecting training data related to a branded phishing URL may comprise retrieving a phishing URL impersonating a brand; fetching a final webpage referenced thereby; determining the main language of the textual content thereof; rendering graphical representation(s) of the final webpage; extracting, from the source of URLs, information including the retrieved phishing URL, a brand, a type and a date associated therewith and storing the extracted information together with the final webpage and the rendered graphical representation(s). A message that contains a URL matching the phishing URL may then be retrieved. The main language of the textual content of the message may be determined and graphical representations thereof rendered. A record may be updated with the message, the main language and the rendered graphical representations, which may be made accessible as training data to train users to recognize phishing websites and messages.