User-Specific Breach Detection Model Segmentation
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Solution Overview
Problem
Conventional security breach detection systems struggle to accurately predict security breaches in user accounts, especially when multiple users share an account or when a user creates an independent account, due to their reliance on generic detection algorithms and failure to account for user-specific behavior.
Innovation Solution
The system generates a user-specific breach detection model by updating a base model with activities associated with a first user, labeling prior activities in a combined account to improve training data, and duplicating models to adapt to new accounts created by users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional generic detection algorithms are used, then system complexity is reduced and ease of operation is improved, but detection precision and reliability deteriorate due to inability to account for user-specific behavior
Solution Approach 1:
The system segments the detection model into a shared base model and user-specific personalized models. The base model handles generic detection patterns while user-specific models capture individual behavior patterns. This segmentation allows the system to achieve high detection accuracy for multiple users without requiring complete model redesign for each user, thus managing complexity while improving precision.
Solution Approach 2:
The system dynamically adjusts model parameters based on user-specific activity patterns. When a user creates a new account, the system updates the personalized model parameters to reflect the user's new behavior patterns. This parameter adaptation enables the system to maintain high detection accuracy across different users and scenarios without requiring complex model retraining from scratch.
2Measurement precision
If user-specific personalized models are created for each user, then detection precision improves by capturing individual behavior patterns, but device complexity and computational resources increase
Solution Approach 1:
The model is divided into a shared base model that is computationally efficient and handles common detection tasks, and lightweight user-specific models that only process data relevant to individual users. This segmentation reduces the overall computational burden compared to creating completely separate models for each user, while still achieving high user-specific detection accuracy.
Solution Approach 2:
The base model serves multiple users universally, handling generic detection patterns that apply across all users. User-specific models then layer on top of this universal foundation to handle individual variations. This multi-functional approach allows the system to achieve high precision for each user while avoiding the computational waste of redundant model structures.
3Measurement precision
If the system trains new models from scratch for each new user account, then detection accuracy for that user improves, but loss of time and productivity decrease due to retraining requirements
Solution Approach 1:
The system performs preliminary action by creating a base model that captures generic detection patterns before user-specific patterns are learned. When a new user account is created, the system uses this pre-existing base model as a foundation, only requiring lightweight updates to capture the new user's specific patterns. This preliminary preparation eliminates the need for complete model retraining and significantly reduces training time while maintaining high accuracy.
Solution Approach 2:
The system dynamically adapts the user-specific model parameters based on the new user's activity patterns rather than training from scratch. This dynamic adaptation approach allows the system to quickly adjust to new users while leveraging the computational efficiency of the shared base model, thereby reducing training time and improving productivity.
4Adaptability or versatility
If rule-based detection systems are used, then ease of operation is improved and system complexity is reduced, but adaptability deteriorates when users create new accounts or change behavior patterns
Solution Approach 1:
The system segments detection functionality into a base model that handles generic patterns and user-specific models that handle individual adaptations. This segmentation provides adaptability to new scenarios like new accounts while keeping each component manageable. The base model maintains simplicity for common cases while user-specific models provide the necessary adaptability when needed.
Solution Approach 2:
The system dynamically switches between using the base model for generic cases and user-specific models for personalized cases. This dynamic approach allows the system to adapt to new scenarios like new accounts by activating the appropriate model level, providing versatility without requiring the entire system to be overly complex.
Data Source
AI summary
Systems and methods for detecting unauthorized access are disclosed herein. In some aspects, the system receives a combined activity dataset. The system updates a base breach detection model based on the combined activity dataset to generate a combined breach detection model. The system duplicates the combined breach detection model to generate a first breach detection model for a first user. The system receives a first activity dataset for the first user and trains a labeling model to associate activities from the first activity dataset with the first user. The system processes the combined activity dataset using the labeling model to associate activities from a portion of the combined activity dataset with the first user. The system updates the base breach detection model based on activities from the first activity dataset and the portion of the combined activity dataset to generate a second breach detection model to detect breach activity.