Metadata-Enriched Authentication for Distributed Network Traffic
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Solution Overview
Problem
Current authentication methods for electronic network traffic are prone to malfeasance due to incomplete or inaccurate data, leading to unauthorized access and potential exposure of sensitive information, resulting in productivity loss and additional costs.
Innovation Solution
A system and method for electronic authentication using enriched data, incorporating a middleware component and machine learning model to collect metadata, recognize patterns, and provide alerts for anomalies, enhancing the authentication process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional authentication methods are used, then the authentication process is simple and fast, but the authentication accuracy is low and prone to malfeasance
Solution Approach 1:
The authentication system is segmented into multiple independent components: traditional authentication module, enhanced authentication engine, middleware, metadata sources, and machine learning model. Each component performs a specific function, allowing the system to achieve high accuracy without requiring complete redesign of the entire authentication process
Solution Approach 2:
The enhanced authentication engine acts as an intermediary between the traditional authentication system and the authorization decision. It receives authentication credentials, enriches them with additional data from metadata sources, applies machine learning analysis, and then passes the enhanced authentication result to the authorization system, thereby improving accuracy without disrupting the existing simple authentication flow
2Reliability
If more authentication data is collected, then the authentication accuracy improves, but the computing resources and processing time increase
Solution Approach 1:
The system applies partial action by selectively collecting metadata from specific sources based on the authentication context and risk level. Not all metadata sources are queried for every authentication attempt - the system intelligently determines which additional data sources to access, collecting enough information to improve accuracy without exhaustively gathering all possible data, thus avoiding excessive computing resource consumption
Solution Approach 2:
The machine learning model is trained on historical authentication data to automatically identify patterns and make authentication decisions. Once trained, the model serves itself by autonomously analyzing authentication requests and determining anomalies without requiring manual intervention or extensive real-time computing resources, reducing ongoing energy consumption while maintaining high accuracy
3Measurement precision
If machine learning model is implemented, then the anomaly detection capability improves, but the device complexity and implementation difficulty increase
Solution Approach 1:
The enhanced authentication engine serves as an intermediary layer that encapsulates the machine learning model's complexity. It handles the integration of the machine learning model with the existing authentication system, managing data flow between metadata sources, the model, and the authorization system. This intermediary approach allows the machine learning model to be implemented without requiring complex modifications to the core authentication infrastructure
Solution Approach 2:
The system implements feedback mechanisms where authentication outcomes and anomaly detections are fed back into the machine learning model for continuous training and improvement. This feedback loop enables the system to automatically refine its anomaly detection precision over time without requiring manual reconfiguration or complex intervention, simplifying the implementation while enhancing measurement precision
Data Source
AI summary
Embodiments of the invention are directed to systems, computer program products, and methods for electronic authentication of electronic distributed network traffic via enriched data. An authorization request is provided from a user of a first endpoint device to send or receive data with a second endpoint device. A request for a first authentication credential to the first endpoint device is transmitted, and an authentication transmission is received by the system. An enhanced authentication engine is initialized, which collects, by a middleware, data flow from an identity database to the enhanced authentication engine. A metadata source is combined with the data flow to form a combined data, which is transmitted to an alert engine.