User Authentication Misuse Detection via Tracer Data Structures
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Solution Overview
Problem
Conventional identity management (IdM) technologies are vulnerable to misuse, particularly in scenarios like phishing attacks and weak password usage, as they fail to detect malicious activities effectively, especially with the increasing complexity of device access and geographical locations, leading to compromised corporate systems.
Innovation Solution
A data-driven user authentication misuse detection system that collects and analyzes user authentication attempts, builds account usage patterns, and detects malicious behavior without requiring training data, using rule-based reasoning to flag suspicious activities and prevent unauthorized access.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional identity management systems are used with strict use policies, then security control is improved, but detection capability against sophisticated phishing attacks deteriorates
Solution Approach 1:
The patent replaces conventional mechanical security controls (firewalls, spam filters) with a data-driven statistical detection system that analyzes user behavior patterns. The system collects authentication data, builds user profiles, and uses statistical analysis to detect anomalies, substituting traditional rule-based security with intelligent pattern recognition that can adapt to sophisticated phishing attacks.
Solution Approach 2:
The system performs self-learning by automatically collecting authentication data and building user behavior profiles without requiring manual training. The statistical models self-adjust to detect anomalies based on observed patterns, enabling the system to autonomously improve its detection capability over time without human intervention.
2Reliability
If conventional security solutions are deployed, then basic protection is provided, but detection of subtle authentication misuse deteriorates
Solution Approach 1:
The patent replaces basic rule-based security checks with sophisticated statistical analysis that measures subtle deviations in user behavior patterns. The system analyzes multiple authentication attributes simultaneously and uses statistical significance testing to detect precise anomalies, enabling detection of subtle misuse that conventional systems miss.
Solution Approach 2:
The system changes the detection parameters from simple binary rules to multi-dimensional statistical metrics. By analyzing the distribution, frequency, and correlation of authentication attributes, the system can detect subtle patterns of misuse that require sophisticated measurement precision rather than basic rule matching.
3Measurement precision
If user authentication data is collected and analyzed to detect misuse, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The patent segments the authentication data collection and analysis into distinct modular components: data collection modules, user profile building modules, statistical analysis modules, and alert generation modules. This segmentation allows each component to be independently optimized and managed, reducing overall system complexity while maintaining high detection accuracy.
Solution Approach 2:
The system reduces operational complexity through self-service automation. The statistical models automatically learn from authentication data without requiring manual training or configuration. The system self-adjusts its detection thresholds and parameters based on observed patterns, eliminating the need for complex manual tuning and reducing operational burden.
Data Source
AI summary
Systems, methods, and other embodiments are disclosed for data-driven user authentication misuse detection. In one embodiment, for a user authentication attempt to access a secure computer resource, user authentication log data having user attribute values is collected. The user authentication log data is transformed into a tracer data structure. The tracer data structure is augmented with timestamp data to generate an event data structure. It is determined whether the tracer data structure matches an existing tracer data structure stored in a rules database and, if not, a novelty flag is set to generate a new user behavior model filter. If the tracer data structure matches the existing tracer data structure: an existing user behavior model filter is applied, issuance of an alarm message or signal is controlled, and the existing user behavior model filter is updated based, at least in part, on the event data structure.