Adaptive Incident Prioritization Engine Using BM25 Security Ranking
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Solution Overview
Problem
Conventional security management systems lack comprehensive computing logic and infrastructure for effective security incident prioritization, leading to inefficiencies in incident ranking and resource allocation, particularly in large Security Operations Centers (SOCs) handling thousands of incidents daily.
Innovation Solution
An adaptive incident prioritization engine employing a modified BM25 algorithm, utilizing local relevance metrics and global rarity metrics, to rank security incidents based on their significance, with a dual job pipeline configuration for historical analysis and real-time processing, ensuring the most critical incidents are prioritized.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional security management systems are used without sophisticated ranking algorithms, then the system complexity and resource investment are reduced, but the incident prioritization effectiveness and analyst productivity deteriorate
Solution Approach 1:
The incident prioritization system is segmented into distinct functional modules: data collection module, feature extraction module, ranking algorithm module, and output module. This segmentation allows the complex system to be managed through independent, manageable components while maintaining high prioritization effectiveness.
Solution Approach 2:
The patent introduces an intermediary ranking algorithm layer that sits between the raw security incident data and the analyst decision-making process. This intermediary automatically processes and ranks incidents using sophisticated algorithms, eliminating the need for analysts to manually evaluate each incident while managing complexity through automation.
2Measurement precision
If sophisticated ranking algorithms are implemented for incident prioritization, then the measurement precision and incident ranking accuracy are improved, but the device complexity and resource investment increase
Solution Approach 1:
The system employs parameter-based ranking algorithms that adjust weighting parameters dynamically based on incident characteristics. By changing parameters such as threat severity weights, frequency factors, and impact multipliers, the system achieves high ranking accuracy without requiring fundamentally complex algorithmic structures.
Solution Approach 2:
The patent replaces manual mechanical analysis processes with automated computational algorithms. Instead of analysts manually evaluating incident parameters, the system uses automated ranking algorithms that compute incident priorities based on predefined parameters and weighted factors, achieving high precision through computation rather than human judgment.
3Loss of time
If manual incident prioritization is performed without automated ranking systems, then the system infrastructure and technology investment are reduced, but the loss of time and analyst efficiency increase
Solution Approach 1:
The system performs preliminary automated ranking of incidents as they are received, before analysts need to review them. By pre-processing and pre-ranking incidents using automated algorithms, the system eliminates time-wasting manual evaluation steps and prepares prioritized incident lists ready for analyst action.
Solution Approach 2:
The incident prioritization system serves itself by automatically ranking and prioritizing incidents without requiring analyst intervention. The automated ranking algorithm independently processes incident data, generates priority scores, and produces ranked output, freeing analysts from time-consuming manual prioritization tasks.
Data Source
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AI summary
Methods, systems, and computer storage media for providing security incident prioritization management using an adaptive incident prioritization engine of a security management system are described. The adaptive incident prioritization engine provides security incident prioritization based on an adaptive incident prioritization (AIP) framework built using a ranking algorithm. In particular, the adaptive incident prioritization framework employs a Best Matching 25 (BM25) algorithm and strategically and programmatically adapts the algorithm (e.g., an adaptive incident prioritization model) to rank security incidents based on a local security incident relevance metric (an adaptation of Term Frequency - TF - in BM25) and a global security incident rarity metric (an adaptation of Inverse Document Frequency - IDF - in BM25) associated with security incidents. A prioritization score for a security incident is calculated based on aggregating weighted frequencies of security incident ranking components (e.g., security incident metadata) within a security incident to determine an overall significance of the security incident.