Social Risk Scoring Engine for Proactive Cyber Threat Detection
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Solution Overview
Problem
Traditional cybersecurity approaches focus on reactive endpoint and network security, failing to predict and prevent cyber threats that exploit social media and social networks, which have evolved to bypass traditional defenses.
Innovation Solution
A predictive and active social risk management technology that uses a scoring algorithm to analyze social entity data, identifying risks before attacks occur by scanning social networks and generating risk scores based on various characteristics, allowing for proactive security actions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional reactive security measures (anti-virus, firewalls) are used to secure endpoints and networks, then device security is improved, but the system cannot predict or prevent social media-based cyber threats that bypass traditional defenses
Solution Approach 1:
The system performs preliminary actions by continuously scanning social networks to identify dormant malicious entities before they initiate attacks. The scoring algorithm pre-assesses risk levels of social entities based on their characteristics and behavior patterns, enabling proactive security measures rather than reactive responses after breaches occur.
Solution Approach 2:
The patent introduces an intermediary scoring algorithm that bridges traditional endpoint security and social media threat detection. This intermediary layer analyzes social entity data, assigns risk scores, and provides predictive security intelligence that enhances both device security and social media threat detection capabilities without requiring complete system redesign.
2Reliability
If continuous scanning of social networks is performed to identify risks before attacks, then predictive security capability is improved, but computational resources and time consumption increase
Solution Approach 1:
The scoring algorithm applies local quality by focusing computational resources on specific high-risk areas within social networks. Instead of uniformly analyzing all social entities, the system identifies and intensively scans entities exhibiting suspicious characteristics or behavior patterns, allocating computational energy selectively to where threats are most likely to emerge.
Solution Approach 2:
The system dynamically changes scanning parameters based on threat intelligence and risk assessments. The scoring algorithm adjusts scan depth, frequency, and intensity according to evolving threat landscapes and identified risk levels, optimizing computational resource usage while maintaining predictive security capability across varying threat conditions.
Data Source
AI summary
A computer implemented method includes receiving instructions from a user for identifying data on one or more social networks, wherein the instructions received from the user comprise one or more conditions and one or more actions, identifying, on the one or more social networks, data that is associated with one or more social entities, determining one or more characteristics of the identified data, determining, based on the one or more characteristics of the identified data, that the identified data meets one or more conditions for identifying data specified in the instructions received from the user, in response to determining that the identified data meets one or more conditions specified in the instructions received from the user, performing one or more actions specified in the instructions received from the user.