Predicting Security Communications via Activity Token Sequences
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Solution Overview
Problem
Conventional data storage systems fail to effectively detect and manage security breaches, particularly those involving complex, time-dependent events or multiple malicious entities, as they lack context and pattern analysis over time, leading to delayed or missed breach detections.
Innovation Solution
The system employs machine learning models, such as vector encoding and contrastive learning models, to translate sequences of user activities into predicted communications, enabling early detection and contextual understanding of security breaches by generating time-ordered sequences of tokens and converting them into natural language predictions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional rule-based detection algorithms are used to evaluate user account history for security breaches, then the system can detect obvious security events, but it fails to account for larger scale patterns in user activity over time and misses complex, time-dependent breach events
Solution Approach 1:
The patent transforms the detection approach by changing parameters from simple rule-based criteria to machine learning models that analyze temporal patterns and sequences of user activities. The system uses contrastive learning models to process activity sequences and predict user communications, enabling detection of complex breach patterns that conventional algorithms miss.
Solution Approach 2:
The patent replaces mechanical rule-based detection systems with machine learning-based predictive systems. Instead of evaluating activities against fixed criteria, the system uses trained models to predict user communications and detect breaches based on learned patterns from historical data, substituting rigid mechanical evaluation with adaptive intelligent analysis.
2Reliability
If the system monitors all user activities in detail to detect security breaches early, then detection accuracy improves, but the computational resources and system complexity increase
Solution Approach 1:
The patent extracts only the most relevant features and patterns from user activity data for analysis. Instead of processing all raw activity data, the system uses machine learning models to identify and extract meaningful patterns that indicate security breaches, reducing computational overhead while maintaining detection reliability.
Solution Approach 2:
The system performs partial analysis by focusing on specific temporal patterns and activity sequences that are most indicative of security breaches. Rather than exhaustively analyzing every possible aspect of user activity, the model concentrates computational resources on the most informative signals for breach detection.
3Difficulty of detecting and measuring
If conventional detection systems flag unexpected IP address changes as potential breaches, then some security events are detected, but the system lacks contextual understanding and generates false positives without user perspective
Solution Approach 1:
The patent implements feedback loops where the system continuously learns from user communications and adjusts its detection patterns. By monitoring actual user responses and communications related to security events, the system refines its understanding of normal versus anomalous behavior, reducing false positives and improving contextual accuracy over time.
Solution Approach 2:
The system uses machine learning models as intermediaries between raw activity data and security conclusions. These models translate complex activity patterns into meaningful security assessments, bridging the gap between raw data and contextual understanding without requiring direct human interpretation of every event.
4Loss of time
If the system uses machine learning models to predict user communications based on activity sequences, then early breach detection is achieved, but the device complexity and implementation difficulty increase
Solution Approach 1:
The patent applies preliminary action by training machine learning models in advance on historical activity data and user communications. The models are pre-trained to recognize breach patterns before actual security events occur, enabling rapid prediction and detection when real-time activities are analyzed, thus reducing detection time without adding runtime complexity.
Data Source
AI summary
Systems and methods for generating communications based on user account activity data are described herein. For example, the system may receive an input activity log and generate a plurality of tokens. The system may generate a time-ordered sequence of tokens based on the plurality of tokens. The system may generate an output vector encoding based on a machine learning model. The system may generate a predicted communication based on a vector encoding model. The system may transmit the predicted communication to a user device.