Incident Alert Rule Weight Tuning to Cut False Positives
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Solution Overview
Problem
Security analysts face challenges with blind spots in attack detection due to gaps in automatic detection, inefficient manual workflows, and alert fatigue from numerous false alarms, leading to prolonged investigation times and missed real threats.
Innovation Solution
An automated system that analyzes event resolutions over time to optimize rule weights, reducing false positives by recommending weight adjustments through a user interface, using Mixed Integer Linear Programming (MILP) to balance true positive detection with false positive minimization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual workflows are used to process potential security threats, then security analysts can investigate each alert in detail, but the investigation time and response time become excessively long
Solution Approach 1:
The system automatically processes security alerts by evaluating them against multiple rules and automatically determining false positives, eliminating the need for manual review of every alert. This self-service approach reduces investigation time while maintaining detection accuracy through systematic rule-based evaluation.
Solution Approach 2:
The patent replaces manual mechanical workflows with an automated computational system that uses rule-based evaluation and machine learning algorithms to process security alerts. This substitution dramatically reduces investigation time while maintaining or improving detection accuracy through consistent, scalable automated analysis.
2Reliability
If numerous security rules are applied to detect all potential threats, then detection coverage is improved, but the number of false alarms increases causing alert fatigue
Solution Approach 1:
The system dynamically adjusts rule weights based on historical performance data and feedback from security analysts. Rules that generate excessive false alarms have their weights reduced, while rules with high detection accuracy maintain or increase their weights. This dynamic adjustment optimizes the balance between detection coverage and false alarm reduction.
Solution Approach 2:
The patent changes the parameter of rule weights to optimize detection effectiveness. By systematically adjusting these weights based on performance metrics and feedback, the system maintains comprehensive detection coverage while minimizing false alarms that cause alert fatigue.
3Object-generated harmful factors
If rule weights are manually adjusted to reduce false positives, then false alarm reduction is achieved, but the tuning process becomes time-consuming and complex
Solution Approach 1:
The system automatically tunes rule weights by analyzing historical alert data, false positive patterns, and feedback from security analysts. This self-service tuning process eliminates the need for manual adjustment while systematically reducing false positives, greatly simplifying the overall process.
Solution Approach 2:
The system incorporates feedback loops where the performance of each rule is continuously monitored and used to automatically adjust its weight. This feedback mechanism enables the system to self-optimize, reducing false positives without requiring complex manual tuning procedures.
4Measurement precision
If security analysts review all alerts manually, then false positives can be identified, but alert fatigue sets in due to the high volume of alerts
Solution Approach 1:
The system extracts and automatically processes false positive alerts through rule-based evaluation and machine learning, separating them from genuine security threats. This extraction allows analysts to focus only on high-priority alerts, maintaining false positive identification accuracy while dramatically improving analyst productivity by eliminating alert fatigue.
Data Source
AI summary
Methods, systems, and computer programs are presented for generating recommendations to update the severity of a rule for incident-detection. One method includes accessing a resolution status for insights generated based on an evaluation of rules, each rule associated with a weight. The method determines, based on the resolution status, if each insight corresponds to a true positive (TP) or a false positive (FP), and optimizing values for the weights of the one or more rules to lower the number of FPs. The optimizing comprises identifying an objective function based on predicted values for the insights and the insights resolution status, identifying one or more constraints, and using a solver to obtain the optimized values for the weights. A recommendation to change the weight associated with at least one rule is presented on a user interface based on the optimized values for the at least one rule.