Interaction Session Verification Using Adaptive Authentication Rules
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Solution Overview
Problem
Existing systems fail to effectively verify the identity of interaction session parameters, particularly when there is a lapse in operation, leading to potential security risks and unsolicited interactions.
Innovation Solution
A computer-based system utilizing a machine learning module to dynamically verify anomalies in interaction sessions by monitoring user activities, accessing interaction databases, and adjusting authentication rules based on pre-generated databases and user responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If authentication rules are dynamically adjusted based on detected anomalies, then security is enhanced and identity verification is improved, but system complexity and processing time increase
Solution Approach 1:
The system dynamically adjusts authentication rules based on real-time anomaly detection. The authentication module modifies verification protocols adaptively, changing parameters such as required verification factors and threshold values according to the assessed risk level of each interaction session, rather than applying static rules
Solution Approach 2:
The patent replaces traditional rule-based authentication systems with a machine learning-based anomaly detection system. The trained model automatically analyzes interaction patterns and determines authentication requirements, substituting manual security configuration with automated intelligent decision-making
2Measurement precision
If machine learning algorithms are used to verify interaction sessions, then detection precision is improved, but processing speed and energy consumption increase
Solution Approach 1:
The system applies partial machine learning verification by first using the trained model to assess risk levels, then applying full verification only when anomalies are detected. For normal interactions, streamlined authentication is used, while suspicious sessions receive comprehensive analysis, avoiding unnecessary processing overhead for legitimate users
Solution Approach 2:
The machine learning model is pre-trained on historical interaction data before deployment. This preliminary training phase enables the system to quickly recognize patterns during actual operation, reducing real-time processing requirements while maintaining high detection accuracy
3Measurement precision
If user activities are continuously monitored, then interaction verification accuracy is improved, but user privacy concerns and system resource consumption increase
Solution Approach 1:
The system extracts only the specific interaction parameters needed for verification (such as interaction timestamps, device identifiers, and session metadata) while leaving other user data private and unmonitored. This selective extraction approach enables verification without comprehensive surveillance of user activities
Data Source
AI summary
In some embodiments, the present disclosure provides an exemplary method that may include steps of obtaining a permission from a user to monitor a plurality of activities; receiving monitoring data of the plurality of activities for a first period of time; verifying at least one common session parameter to identify an incoming interaction sessions; utilizing a software application to access an interaction session database; identifying an interruption associated with the software application for a second period of time; utilizing a machine learning algorithm to verify the interruption; determining a plurality of authentication rules; adjusting the plurality of authentication rules, receiving a response to an interactive communication; and updating the database of known session interaction parameters.