Predictive Dark Web Risk Scoring for Compromised PII Detection
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Solution Overview
Problem
The Dark Web serves as a rampant source of illegal activities involving compromised Personally Identifiable Information (PII), which is difficult to monitor and poses a significant risk for identity theft and fraudulent activities, with a majority of such cases occurring among individuals aged 20-49, leading to substantial financial losses and increased fraud rates.
Innovation Solution
A system and method to generate a Dark Web Risk Score (DWRS) using predictive models trained with machine learning algorithms, analyzing PII from various web sources, including the Dark Web, Deep Web, and Surface Web, to identify potential fraudulent activity and provide risk scores and recommendations to users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the Dark Web is monitored to detect compromised PII, then fraud detection capability is improved, but monitoring difficulty increases due to anonymous nature and lack of centralization
Solution Approach 1:
The patent uses data brokers and third-party services as intermediaries to access Dark Web data. These intermediaries aggregate compromised PII from multiple dark web sources and present it in a structured format that can be analyzed, thereby solving the monitoring difficulty while maintaining fraud detection capability
Solution Approach 2:
The system segments the monitoring task into multiple components: data collection from various web sources, data processing and normalization, risk scoring, and reporting. This segmentation allows each component to be optimized independently, reducing overall monitoring difficulty while improving detection reliability
2Measurement precision
If comprehensive PII data is collected from multiple web sources, then risk assessment accuracy is improved, but data processing complexity increases
Solution Approach 1:
The patent combines data from multiple web sources (Surface Web, Deep Web, and Dark Web) into a unified risk assessment model. By merging these diverse data sources and processing them through a single predictive analytics platform, the system achieves comprehensive risk assessment while managing complexity through integration rather than separate processing systems
Solution Approach 2:
The system transforms raw PII data from multiple sources into standardized risk score parameters. By changing the data representation from raw information to normalized risk metrics, the system improves assessment accuracy while reducing processing complexity through parameter standardization
3Speed
If real-time monitoring of Dark Web is implemented, then fraud prevention timeliness is improved, but computational resources required increase
Solution Approach 1:
The system implements periodic risk assessments rather than continuous real-time monitoring. Predictive models are updated at scheduled intervals with newly collected data, providing timely fraud prevention while reducing computational resource consumption compared to continuous real-time analysis
Solution Approach 2:
The patent performs preliminary data collection and processing during off-peak periods, preparing risk assessments in advance. This preliminary action allows the system to provide timely fraud prevention alerts while distributing computational load, reducing peak resource requirements
Data Source
AI summary
One or more implementations include methods, systems, and/or devices to help protect consumers from fraudulent activity using compromised PII. For example, systems and methods can be implemented that enable the detection and prevention of consumer-focused identity theft, generates a risk score and is powered by a predictive model using machine learning techniques and tools and presents information and recommended action a user can take in reports.