Heuristic Generation for Behavioral Anomaly Detection
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Solution Overview
Problem
Current security systems fail to detect unauthorized data theft and atypical user behavior in a timely manner, especially when legitimate users engage in unauthorized activities, as they rely on pre-computed signatures and multi-factor authentication methods that can be costly and inflexible.
Innovation Solution
A heuristic generation method that creates a core cognitive fingerprint by analyzing system data through pattern recognizers, organizing data into geometric structures, determining base attributes, and identifying trends, allowing for the detection of unauthorized behavior and data theft over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If pre-computed signatures are used to identify viruses and threats, then detection accuracy is improved, but response time deteriorates because there is not enough time for a threat to be found, analyzed, and an update delivered
Solution Approach 1:
The system performs preliminary analysis by collecting baseline data about normal system operations, user behaviors, and application patterns before threats occur. This pre-computed behavioral profile enables rapid detection of deviations without requiring time-consuming signature updates when new threats emerge
Solution Approach 2:
The patent replaces the traditional mechanical signature-matching system with a behavioral analysis approach that uses machine learning models to detect anomalies. Instead of relying on pre-computed signatures that require manual updates, the system continuously learns normal patterns and automatically detects deviations, eliminating the time delay associated with signature distribution
2Reliability
If multi-factor authentication schemes such as retina and fingerprint scans are implemented, then identity validation is improved, but system cost and complexity deteriorate
Solution Approach 1:
The system introduces behavioral biometrics as an intermediary layer that analyzes subtle patterns in user interactions with the system. Rather than requiring expensive hardware like retina or fingerprint scanners at every endpoint, the behavioral analysis engine uses software-based monitoring of typing patterns, mouse movements, and application usage to validate user identity and detect unauthorized access
Solution Approach 2:
The patent creates a behavioral copy or fingerprint of legitimate user patterns through continuous monitoring and analysis. This behavioral profile serves as a virtual representation of authorized users, enabling the system to distinguish between legitimate and unauthorized access without requiring physical biometric verification at each access point
3Ease of operation
If access control limitations such as attaching permissions to data are implemented, then authorization control is improved, but protection against data theft by authorized users deteriorates
Solution Approach 1:
The system implements continuous feedback monitoring of user behaviors, application usage patterns, and data access methods. By comparing real-time observations against established baseline patterns, the system can detect when authorized users deviate from normal behavior indicative of data theft, such as unusual data access volumes, atypical application sequences, or abnormal interaction patterns, and trigger appropriate responses
4Measurement precision
If neural network based recognition systems with deep structures are used to capture pattern information, then detection capability is improved, but learning speed and system complexity deteriorate
Solution Approach 1:
The patent segments the behavioral analysis into distinct components: data collection, baseline pattern establishment, real-time monitoring, and anomaly detection. Each component processes specific aspects of user behavior independently, allowing the system to achieve comprehensive pattern recognition without requiring a single complex deep neural network structure
Solution Approach 2:
The system changes the parameters of analysis by focusing on specific behavioral metrics such as typing speed, mouse movement patterns, application switching frequency, and data access timing. By optimizing for these specific parameters rather than attempting comprehensive deep pattern analysis across all possible dimensions, the system achieves effective detection with reduced computational complexity
Data Source
AI summary
A system and method for generating a heuristic is provided. A heuristic is capable of identifying data patterns. The method includes: extracting a data set from multiple input sources; creating a set of unique elements used across the data set; organizing the data set into a geometric structure; grouping portions of the data in the geometric structure into a plurality sub geometric structures; determining base attributes for each sub geometric structure using the set of unique elements; identifying trends in the base attributes among the sub geometric structures; and outputting the heuristic as a combination of the base attributes and the trends.