Social Entity Profile Scoring for Predictive Threat Detection
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Solution Overview
Problem
Traditional cybersecurity approaches focus on reactive measures that are inadequate against evolving cyber threats, particularly those exploiting social media and networks, which require predictive and proactive security to identify and mitigate risks before attacks occur.
Innovation Solution
An active social risk defense engine paired with a predictive analysis framework uses a scoring algorithm to assess risks by analyzing characteristics of social entities, URLs, files, and communications, comparing scores to thresholds to initiate security actions, such as alerts or blockages, to protect individuals and organizations from social-based threats like impersonation, fraud, and social engineering.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional reactive security measures (anti-virus, firewalls) are used to secure endpoints and networks, then system security is maintained through perimeter defense, but the system cannot proactively identify dormant malicious entities before they initiate attacks
Solution Approach 1:
The system performs preliminary actions by proactively scanning social networks to identify dormant malicious entities before they can initiate attacks. The predictive analysis framework continuously monitors and assesses potential threats in advance, generating risk scores and alerts before actual cyber-attacks occur, thus preventing harm rather than merely responding to it.
Solution Approach 2:
The patent introduces an intermediary predictive analysis framework that acts as a mediator between traditional reactive security systems and emerging social media-based threats. This framework analyzes social network data, entity characteristics, and behavioral patterns to generate predictive risk assessments, bridging the gap between conventional security measures and modern social engineering threats.
2Adaptability or versatility
If social media and networks are expanded for communication and information sharing, then connectivity and information access are improved, but information security risk increases due to targeted attacks, fraud, and impersonation
Solution Approach 1:
The system converts the harmful expansion of social media into a beneficial security mechanism by leveraging social network data and entity interactions as the basis for predictive threat detection. The same social connectivity that enables fraud and impersonation also provides the data footprint necessary for identifying and neutralizing malicious entities before they exploit these channels.
Solution Approach 2:
The patent replaces traditional mechanical perimeter-based security defenses with an intelligent, data-driven predictive analysis system. Instead of relying on static firewalls and anti-virus signatures, the system uses machine learning algorithms and predictive modeling to dynamically assess and respond to social engineering threats, substituting mechanical security controls with adaptive intelligent analysis.
3Reliability
If predictive analysis framework is implemented to identify dormant malicious entities, then security threats can be detected before attacks occur, but system complexity increases due to data collection and analysis requirements
Solution Approach 1:
The predictive analysis framework is segmented into distinct functional modules: data collection components that gather social network information, analysis components that assess entity characteristics and generate risk scores, and response components that initiate security actions. This segmentation allows the complex predictive system to be managed as manageable, independent modules rather than a monolithic complex system.
4Measurement precision
If profile scoring and comparison algorithms are used to identify imposters, then impersonation detection accuracy is improved, but processing time and computational resources increase
Solution Approach 1:
The system applies partial action by focusing profile scoring and comparison algorithms only on entities that exhibit suspicious characteristics or fall into high-risk categories identified through preliminary filtering. Rather than exhaustively analyzing all social media entities, the system concentrates computational resources on partial subsets most likely to be malicious, achieving high detection accuracy without proportionally increasing processing time for the entire population.
Data Source
AI summary
A method includes identifying data on a social network that is associated with a suspect social entity, and determining one or more characteristics of the identified data. A reference to the identified data is generated for each of the one or more characteristics. One or more of the generated references are compared to one or more stored references, where the one or more stored references are associated with a protected social entity. A profile score for the suspect social entity is determined based on the comparison. Determining the profile score includes identifying a match between one or more of the generated references and one or more of the stored references.