Social Network Risk Scanning for Malicious Account Detection
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Solution Overview
Problem
Traditional cybersecurity measures are reactive and ineffective against modern cyber threats that exploit social media and networks, failing to predict and prevent attacks from dormant malicious entities.
Innovation Solution
A predictive and active social risk management system that scans social networks, analyzes data using machine learning techniques, assigns risk scores, and generates alerts or takes security actions to identify and mitigate potential threats before they occur.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional reactive security measures (anti-virus, firewalls) are used, then endpoint and network security is maintained, but predictive protection against social media-based cyber threats is lost
Solution Approach 1:
The system performs preliminary actions by continuously scanning social networks and calculating risk scores for social entities before they can initiate attacks. The predictive risk protection module identifies dormant malicious entities and potential threats in advance, allowing security measures to be taken proactively rather than reactively after an attack occurs.
Solution Approach 2:
The system changes the parameter of threat detection from reactive endpoint-based detection to proactive risk scoring based on social entity behavior patterns. By analyzing multiple parameters including account age, posting frequency, network connections, and content characteristics, the system transforms social media data into predictive risk assessments that complement traditional security measures.
2Reliability
If social network scanning and analysis is performed, then predictive threat identification is improved, but system complexity and computational resources increase
Solution Approach 1:
The system segments the complex security analysis task into distinct functional modules: a scanning module that collects social network data, a risk calculation module that computes risk scores based on multiple factors, and a predictive protection module that generates alerts. This segmentation allows each component to be optimized independently and managed separately, reducing overall system complexity.
Solution Approach 2:
The patent introduces an intermediary risk score calculation mechanism that mediates between raw social network data and security decisions. Instead of directly analyzing complex social media patterns, the system uses the risk score as an intermediary metric that simplifies threat assessment and enables automated predictive protection decisions without requiring direct interpretation of complex social behavior patterns.
3Measurement precision
If comprehensive data scanning and machine learning analysis are performed, then threat detection accuracy is improved, but processing time and computational energy consumption increase
Solution Approach 1:
The system applies partial action by focusing computational resources on calculating risk scores for specific social entities that meet certain criteria or exhibit suspicious patterns, rather than performing exhaustive analysis on all social network data. This selective approach maintains detection accuracy for high-risk targets while reducing overall computational energy consumption.
Solution Approach 2:
The risk scoring system operates autonomously using automated algorithms that continuously assess social entities without requiring manual intervention. The machine learning models self-adjust and retrain based on new data, reducing the need for energy-intensive manual analysis while maintaining high assessment accuracy through automated, scalable computational processes.
Data Source
AI summary
A computer-implemented method including scanning, by one or more processors, data that is maintained on one or more social networks, identifying, one or more social entities associated with the scanned data, determining a risk score for each of the one or more social entities, analyzing the scanned data using one or more machine learning techniques, assigning a rating to each of the one or more social entities based on the determined risk score and the analyzed data, and determining, based on the rating assigned to at least one of the one or more social entities that the at least one social entity is a security risk.