Phishing Engine for Dormant Threat Detection
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Solution Overview
Problem
Traditional cybersecurity approaches are reactive and fail to predictively address evolving cyber threats that exploit social media and social networks, leading to increased risks for individuals and organizations.
Innovation Solution
A predictive and active social risk management system that uses a scoring algorithm to assess vulnerabilities by analyzing social entity interactions, generating reports, and initiating security actions based on risk thresholds, including the use of a phishing engine to track and mitigate phishing attempts and impersonation threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional reactive security measures are used, then endpoint and network security are maintained, but predictive identification of dormant malicious entities is lost
Solution Approach 1:
The system performs preliminary actions by proactively generating test hyperlinks and scanning social networks to identify dormant malicious entities before they can initiate attacks. The phishing engine creates test links and monitors their selection by social entities, enabling early detection and prediction of potential threats rather than waiting for reactive responses after breaches occur.
2Measurement precision
If social media platforms are monitored continuously, then predictive threat identification is improved, but system complexity and resource consumption increase
Solution Approach 1:
The system introduces a phishing engine as an intermediary component that acts as a mediator between the monitoring system and social networks. This engine generates test hyperlinks and facilitates controlled interactions with social entities, enabling precise threat detection through structured experiments rather than unstructured continuous monitoring, thereby managing system complexity.
Solution Approach 2:
The system creates copies of potential phishing scenarios by generating test hyperlinks that mimic real phishing attempts. These synthetic test cases allow the system to measure and analyze entity behavior in controlled conditions, improving detection accuracy without requiring direct monitoring of all actual social media interactions.
3Measurement precision
If hyperlinks are generated and distributed to assess vulnerability, then predictive risk assessment is improved, but exposure to phishing attempts increases
Solution Approach 1:
The system converts the potential harm of phishing exposure into a benefit by using controlled test hyperlinks to assess vulnerability. Instead of avoiding all phishing-like content, the system deliberately introduces benign test links that simulate phishing attempts, allowing it to measure and improve security posture by transforming the harmful exposure into a diagnostic tool.
Data Source
AI summary
A computer-implemented method includes generating, by one or more processors, a hyperlink targeting a Uniform Resource Locator (URL), detecting a selection of the generated hyperlink by one or more social entities across one or more social networks, generating a report, wherein the generated report includes analytical details regarding the selection of the generated hyperlink by the one or more social entities, and providing the generated report to a user associated with a protected social entity.