Credential Usage Anomaly Detection via Behavioral Baseline Modeling
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Solution Overview
Problem
Current security systems lack effective methods to detect changes in user credential usage that indicate potential credential compromise, particularly in scenarios where users are assigned more privileges than needed, leading to potential unauthorized access.
Innovation Solution
A method and system that extracts and analyzes credential usage events from a data network, creating records and assigning labels to fit a predictive model, which filters subsequent data to identify suspicious sequences of events and generates alerts when compromised access is detected.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If users are assigned more privileges than needed for their roles, then access flexibility and operational efficiency are improved, but the risk of unauthorized access and credential compromise increases
Solution Approach 1:
The system performs preliminary actions by establishing baseline credential usage patterns during a training period before actual monitoring begins. This proactive approach allows the system to learn normal behavior patterns in advance, enabling it to detect anomalies more effectively when they occur, thus maintaining operational flexibility while mitigating unauthorized access risks
Solution Approach 2:
The system implements continuous feedback loops by monitoring credential usage in real-time, comparing it against established baselines, and generating alerts when deviations are detected. This feedback mechanism enables the system to maintain high privilege levels for operational efficiency while providing immediate notification of potential security incidents, thus resolving the contradiction between access flexibility and security risk
2Device complexity
If traditional security monitoring methods are used, then system simplicity is maintained, but the ability to detect subtle credential misuse changes is insufficient
Solution Approach 1:
The system transitions from static security rules to dynamic behavioral analysis by continuously learning and adapting to each user's credential usage patterns. This dynamic approach enables detection of subtle misuse changes that fixed rules would miss, while the automated learning process prevents the system from becoming overly complex for manual management
Solution Approach 2:
The system performs self-service by automatically establishing baselines, learning user patterns, and detecting anomalies without requiring constant manual configuration or intervention. This automation maintains relative system simplicity while achieving high detection precision through continuous autonomous analysis of credential usage behaviors
3Measurement precision
If comprehensive user behavior monitoring is implemented, then detection accuracy for credential compromise is improved, but data processing complexity and computational resources increase
Solution Approach 1:
The system extracts and focuses only on the most relevant features from comprehensive user behavior data, such as login times, accessed resources, and command patterns. By selecting only the critical indicators of credential misuse rather than processing all possible data points, the system maintains high detection accuracy while reducing overall data processing complexity
Solution Approach 2:
The monitoring system segments user behavior analysis into distinct components: authentication events, resource access patterns, and command execution behaviors. This segmentation allows the system to process comprehensive data more efficiently by handling each behavior type separately with specialized analysis methods, thus improving detection accuracy without proportionally increasing overall system complexity
4Reliability
If real-time analysis of all user events is performed, then immediate detection of credential misuse is achieved, but system performance and processing speed deteriorate
Solution Approach 1:
The system applies partial monitoring by focusing computational resources on analyzing only those events that deviate from established baselines or show signs of potential misuse. Rather than performing exhaustive real-time analysis on every single user event, the system selectively intensifies analysis only when necessary, maintaining immediate detection capability for actual threats while preserving overall system processing speed
Data Source
AI summary
A method including extracting, from initial data transmitted on a network, multiple events, each of the events including a user accessing a resource. First and second sets of records are created, each first set record including a sub-group of the events of a user, each second set record including a sub-group of the events of a multiple users during respective sub-periods of a training period. Safe labels are assigned to the first set records and suspicious labels are assigned to the second set records. An analysis fits, to the first and the second set records and their respective labels, a model for predicting the label for a given record. The model filters subsequent network data to identify, in the subsequent data, sequences of events predicted to be labeled suspicious by the model, and upon detecting a given sequence of events predicted as suspicious by the model, an alert is generated.