Automated Rule Generation for Event Detection Systems
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Solution Overview
Problem
Manual generation of logical rules for event detection systems is time-consuming, subjective, and prone to errors, leading to poor accuracy and missed events, especially in dynamic environments where new events and threats emerge frequently.
Innovation Solution
A rule-building engine that automatically generates logical rules by training decision trees, extracting rules from these trees, and applying performance metrics to select the most effective rules for event detection, with the ability to predict rule efficacy and update rules periodically.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual generation of logical rules is used for event detection systems, then human expertise and judgment can be applied, but the process becomes time-consuming and prone to errors
Solution Approach 1:
The system performs self-training by automatically generating logical rules from training data without requiring continuous human intervention. The machine learning model trains itself on historical event data, extracting patterns and generating detection rules autonomously, thereby resolving the contradiction between accuracy and time consumption.
Solution Approach 2:
The system performs preliminary training using historical training data before actual event detection is needed. By pre-training the model and generating rules in advance, the system prepares detection capabilities beforehand, reducing the time required during actual operation while maintaining high accuracy through thorough preliminary analysis.
2Reliability
If manual generation of logical rules is used, then flexibility in rule creation is maintained, but subjectivity and human error increase
Solution Approach 1:
The patent replaces the manual mechanical process of rule creation with an automated machine learning system. The computer automatically processes training data, identifies patterns, and generates logical rules without human intervention, eliminating subjectivity and human error while maintaining consistency across all generated rules.
Solution Approach 2:
The system incorporates feedback mechanisms where the generated rules are evaluated against validation data, and the model is iteratively improved based on performance metrics. This feedback loop ensures high reliability by continuously optimizing rule accuracy while the automated process manages complexity through systematic evaluation criteria.
3Adaptability or versatility
If comprehensive rule sets are created to cover all possible events, then detection coverage is improved, but false positives increase
Solution Approach 1:
The system dynamically adjusts parameters such as detection thresholds and rule weights based on the specific characteristics of different events and contexts. By changing these parameters adaptively, the system maintains broad coverage for diverse event types while reducing false positives through context-sensitive threshold adjustment and parameter optimization.
Solution Approach 2:
Different logical rules and detection parameters are applied to different types of events based on their specific characteristics. The system assigns specialized rules and adjusted parameters for each event category, ensuring high precision for each specific event type while maintaining overall comprehensive coverage across all event types.
Data Source
AI summary
Logical rules can be automatically generated for use with event detection systems according to some aspects of the present disclosure. For example, a computing device can extract a group of logical rules from trained decision trees and apply a test data set to the group of logical rules to determine count values corresponding to the logical rules. The computing device can then determine performance metric values based on the count values, select a subset of logical rules from among the group of logical rules based on the performance metric values, and provide at least one logical rule in the subset for use with an event detection system. The event detection system can be configured to detect an event in relation to a target data set that was not used to train the decision trees.