Security Detection Rule Scoring for False Alert Reduction
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Solution Overview
Problem
Existing security detection rules often fail to adapt to changing network threats over time, leading to inefficiencies and potential false alerts, as they are not continuously evaluated for performance and modification needs.
Innovation Solution
A system and method for continuously evaluating and scoring security detection rules based on various factors, including frequency of triggering, outcomes, and historical performance, to identify rules that need attention or modification, with visual feedback to users for dynamic adjustment.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If security detection rules are continuously monitored and evaluated, then rule performance and effectiveness improve, but system complexity and resource consumption increase
Solution Approach 1:
The system implements continuous feedback loops by monitoring rule triggering frequencies, alert outcomes, and performance metrics. This feedback enables automatic scoring and ranking of rules, allowing the system to adapt and improve rule effectiveness without manual intervention while managing complexity through automated processes.
Solution Approach 2:
The evaluation system performs self-assessment by automatically scoring and ranking its own detection rules based on predefined criteria. The system independently identifies underperforming rules and generates recommendations for modification, reducing the need for external monitoring and simplifying overall system management.
2Adaptability or versatility
If detection rules are frequently updated and modified, then responsiveness to new threats improves, but system stability and false alert rates worsen
Solution Approach 1:
The system performs preliminary evaluation and scoring of detection rules before deployment or modification. By assessing rules against historical data and threat patterns in advance, the system ensures that updates maintain stability while improving threat response capability, preventing premature or inappropriate rule changes.
Solution Approach 2:
The system dynamically adjusts detection rules based on evolving threat landscapes while maintaining stability through continuous performance monitoring. Rules are modified only when evaluation metrics indicate genuine improvement potential, allowing the system to adapt to new threats without sacrificing overall stability.
3Measurement precision
If comprehensive rule evaluation is performed, then detection accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs partial evaluation by focusing computational resources on the most critical aspects of rule performance, such as triggering frequency and alert outcomes. Rather than comprehensively analyzing every possible metric, the system identifies and evaluates key performance indicators that provide sufficient detection accuracy while minimizing processing time.
Solution Approach 2:
The system implements periodic evaluation cycles for detection rules, assessing performance at scheduled intervals rather than continuously. This periodic approach maintains detection accuracy by regularly updating rule evaluations while significantly reducing computational overhead and processing time compared to continuous comprehensive analysis.
Data Source
AI summary
Systems and methods disclosed can evaluate security detection rules in a network security computing environment. Results for a processed log of security events can be retrieved. The results can identify determined outcomes for instances triggering security detection rules. The security detection rules can detect specific behavior on a network by being processed against a log of security events. Scores for the security detection rules can be determined based on the results of the processed log of security events and the determined outcomes. The security detection rules can be ranked based on the scores, from highest to lowest score. The highest score can indicate that a corresponding rule is performing worst among the security detection rules and the lowest score can indicate that a corresponding rule is performing best among the security detection rules. A rules score report can be generated based on the ranked rules.