IP Reputation Scoring via Behavioral Interaction Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods face challenges in automatically detecting and preventing online fraud, as different fraud modes exhibit varying activity patterns, making it difficult to characterize abusive IP addresses effectively.
Innovation Solution
The system automatically determines the reputation of an IP address by analyzing interaction history, measuring both aggregate and negative behavior, and calculating a reputation score, which can be used to inform authorization decisions and security measures.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If third party whitelists and blacklists are used to characterize IP addresses, then some level of fraud detection is achieved, but the detection precision is insufficient due to varying activity patterns of different fraud modes
Solution Approach 1:
The system changes the parameters used for IP address characterization from static whitelist/blacklist categories to dynamic behavioral parameters including interaction counts, connection patterns, data transfer volumes, and temporal activity profiles. This allows the same IP address to be evaluated differently based on its actual behavior rather than predefined categories, resolving the contradiction between detection precision and adaptability to different fraud modes
Solution Approach 2:
The system transitions from static IP reputation lists to dynamic behavioral analysis that continuously monitors and adapts to changing activity patterns. By evaluating multiple behavioral dimensions in real-time and updating reputation scores dynamically, the system can adapt to various fraud modes while maintaining high detection precision across different attack vectors
2Productivity
If automated detection systems are implemented to identify abusive IP addresses, then detection speed is improved, but the complexity of the detection system increases
Solution Approach 1:
The detection system is segmented into modular components: data collection modules that gather interaction information, analysis modules that evaluate specific behavioral parameters, scoring modules that calculate reputation scores, and decision modules that trigger security actions. This segmentation enables automated high-speed detection while managing complexity through modular design, allowing each component to be independently optimized and maintained
Solution Approach 2:
The system implements feedback loops where detection results and security actions are fed back into the analysis process to continuously refine reputation scores. This automated feedback mechanism improves detection speed by learning from past fraud patterns while managing complexity through algorithmic automation rather than manual rule updates
Data Source
AI summary
Techniques are provided for determining a reputation of a source address based on analytics of interaction history. In an embodiment, computers store interaction data that indicates a plurality of interactions between users and an online entity. For each interaction of the plurality of interactions, the interaction data indicates a source address of a user. For each source address of a plurality of source addresses indicated in the interaction data, the computers determine an aggregate measurement indicating aggregate behavior of users associated with an aggregate subset of interactions of the plurality of interactions. Each interaction of the aggregate subset is associated with said source address. The computers determine a negative measurement indicating negative behavior of users that are associated with a negative subset of interactions of the aggregate subset. The computers generate, based on the negative and aggregate measurements, a score that indicates a reputation of said each source address.