Automated Malicious Activity Detection Rule Ranking
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Solution Overview
Problem
Conventional intrusion detection systems rely heavily on expert knowledge and experience, leading to vulnerabilities and inefficiencies, especially when managed by less-experienced personnel, and lack automation for adapting to evolving threats, resulting in sub-optimal configurations.
Innovation Solution
An automated, data-driven method for creating, evaluating, and recommending malicious activity detection rules using electronic circuitry, which evaluates and ranks rules based on performance metrics such as precision, recall, and correlation, reducing reliance on expert skill and knowledge.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If expert knowledge and experience are used to define detection rules, then detection accuracy is improved, but system complexity and burden on experts increase
Solution Approach 1:
The system automatically generates detection rules by analyzing historical security data and threat patterns without requiring manual expert configuration. The rule generation module autonomously creates detection rules based on learned patterns, reducing expert burden while maintaining detection accuracy through data-driven insights
Solution Approach 2:
Manual expert analysis and rule creation processes are replaced with automated machine learning algorithms that process security data and generate detection rules. This substitution transforms the mechanical process of manual rule definition into an automated computational process, reducing complexity while preserving detection effectiveness
2Reliability
If manual research and trial-and-error are used to update policies, then detection effectiveness is improved, but time consumption and productivity decrease
Solution Approach 1:
The system continuously analyzes historical security data and threat patterns in advance to pre-generate detection rules before threats materialize. By performing preliminary analysis on emerging threat patterns, the system prepares detection rules proactively, enabling rapid response to new threats without manual trial-and-error processes
Solution Approach 2:
The system implements continuous feedback loops where detection results, false positives, and threat outcomes are automatically analyzed to refine and update detection rules. This automated feedback mechanism continuously improves detection effectiveness while maintaining high update speed through systematic learning from operational data
3Productivity
If automated rule generation is implemented, then productivity and adaptability are improved, but measurement precision and reliability may worsen
Solution Approach 1:
An evaluation module serves as an intermediary between automated rule generation and deployment. This intermediary assesses generated rules against multiple criteria including false positive rates, detection coverage, and threat relevance before rules are activated, ensuring automated rules meet precision standards while maintaining high productivity
Solution Approach 2:
The system generates multiple candidate detection rules beyond what is immediately needed, then evaluates and selects the most effective subset. By producing excessive candidate rules and filtering them through evaluation, the system ensures high detection accuracy while maintaining efficient rule deployment through selective activation of only the most effective rules
4Ease of operation
If expert dependency is reduced through automation, then ease of operation is improved, but the system becomes more complex
Solution Approach 1:
The automated rule generation system serves multiple functions including data analysis, pattern recognition, rule creation, and evaluation within a single integrated platform. This multi-functional approach consolidates complex automation capabilities into one unified system that remains easy to operate while reducing expert dependency across various security tasks
Data Source
AI summary
A computer-implemented technique provides rules for use in a malicious activity detection system. The technique involves performing evaluation operations on a plurality of malicious activity detection rules. The technique further involves ranking the plurality of malicious activity detection rules in an order based on results of the evaluation operations (e.g., sorting the rules systematically in an order based on measures such as precision, recall, correlation to other rules already in use, etc.). The technique further involves, based on the order of the plurality of malicious activity detection rules, providing a malicious activity detection rule report which recommends a set of malicious activity detection rules of the plurality of malicious activity detection rules for use in the malicious activity detection system.