Network Fraud Scoring Using Device Fingerprints and Behavior
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Solution Overview
Problem
Existing network operation systems struggle to distinguish between malicious and non-malicious entities, leading to unauthorized access and significant resource consumption, as they primarily focus on validating individual requests rather than evaluating the entity's authenticity.
Innovation Solution
Implement a server-based machine learning model that generates a likelihood of fraud by analyzing digital fingerprints, network operation metrics, and risk factors, enabling entity-specific mitigation actions and dashboard interfaces for enhanced security.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the server checks every request to determine authorization, then the server can prevent unauthorized access, but significant computing resources are consumed
Solution Approach 1:
The server performs preliminary actions by evaluating entity fingerprints and historical behavior patterns before processing individual requests. This pre-evaluation of entity trustworthiness allows the server to make informed decisions about which requests require full authentication checks and which can be rapidly rejected, reducing overall computing resource consumption while maintaining security.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring entity behavior, updating fingerprints, and adjusting risk assessments based on historical data. This feedback loop allows the server to learn from past interactions and improve its ability to distinguish legitimate from malicious entities over time, enhancing detection accuracy without requiring exhaustive checks for every request.
2Reliability
If the server checks every request for authorization, then security is maintained, but the server cannot distinguish between malicious and non-malicious entities transmitting authorized requests
Solution Approach 1:
The system creates a virtual representation (copy) of the entity through its fingerprint and behavioral profile. This copy contains aggregated information about the entity's historical interactions, patterns, and characteristics, allowing the server to assess entity authenticity without analyzing each individual request in detail. The fingerprint serves as a condensed summary that captures essential entity traits for differentiation.
Solution Approach 2:
The security evaluation process is segmented into multiple components: entity fingerprint analysis, historical behavior assessment, risk factor evaluation, and request-specific validation. This segmentation allows the server to handle different aspects of security evaluation separately, improving its ability to detect entity authenticity while maintaining efficient request processing through specialized analysis of each component.
3Reliability
If the server blocks access to resources for suspected malicious entities, then security is improved, but legitimate entities may be incorrectly blocked
Solution Approach 1:
The system dynamically adjusts access control decisions based on real-time risk assessments and entity behavior patterns. Rather than applying static blocking rules, the server continuously evaluates entity fingerprints, historical behavior, and current request context to make adaptive decisions. This dynamic approach allows legitimate entities with established trust profiles to maintain access while providing flexible protection against evolving malicious patterns.
Solution Approach 2:
The system changes parameters such as risk thresholds, evaluation weights, and access control decisions based on entity-specific characteristics and contextual factors. By adjusting these parameters dynamically for each entity and request scenario, the server can fine-tune the balance between security protection and access continuity, reducing false positives while maintaining strong security postures.
Data Source
AI summary
Presented herein are systems and methods of training machine learning (ML) models to determine likelihoods of fraud in network operations caused by computing systems. A server may generate training data to include (i) a digital fingerprint associated with an identity of a computing system of a plurality of computing systems and (ii) a plurality of network operation metrics associated with the computing system. The server may label the training data to indicate whether fraudulence is caused by the computing system. The server may execute, using the training data, a ML model having a plurality of weights to generate a likelihood of fraud caused by the computing system. The server may compare the likelihood of fraud with labeled training data to determine an error metric in accordance with a loss function. The server may update at least one of the plurality of weights using the error metric.