Automated Security Rule Generation for Containerized Applications
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Solution Overview
Problem
Existing security monitoring systems for computer program applications require manual definition and continuous updating of rules, leading to high maintenance costs and potential for false positives or false negatives, especially in containerized environments where inter-program communications are constrained.
Innovation Solution
A data processing method that automatically generates programmed rules to detect anomalous operations in computer systems executing containerized applications by aggregating operation datasets, scoring operation properties, and creating baseline operation rules based on score threshold values.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If manual rule definition is used for security monitoring, then rule accuracy can be controlled, but maintenance cost and complexity increase significantly
Solution Approach 1:
The system automatically generates security rules by analyzing application behavior patterns and operational datasets without requiring manual definition. The rule generation process self-adjusts to new application versions and container configurations, eliminating the need for continuous manual maintenance while maintaining high accuracy through automated learning from operational data.
Solution Approach 2:
The system dynamically changes rule parameters based on observed application behavior patterns. By continuously analyzing operational datasets and updating rule parameters automatically, the system adapts to new security threats and application versions without manual intervention, reducing maintenance complexity while preserving rule accuracy.
2Adaptability or versatility
If manually defined rules are used for each unique program, then security coverage can be tailored, but continuous updating is required when programs change
Solution Approach 1:
The system continuously monitors application behavior and uses this feedback to automatically update security rules. When applications change versions or behavior patterns, the system receives feedback from operational datasets and automatically adjusts rules to maintain comprehensive security coverage without manual updating.
Solution Approach 2:
The system performs preliminary analysis of application behavior patterns and pre-generates appropriate security rules before security incidents occur. By proactively analyzing operational data and anticipating new behavior patterns, the system maintains up-to-date security coverage without requiring reactive manual updates.
3Measurement precision
If manually maintained rules are used, then rule specificity can be controlled, but false positives and false negatives increase due to staleness
Solution Approach 1:
The system continuously analyzes operational datasets and updates security rules without interruption. This continuous action ensures rules remain current with application behavior patterns, preventing staleness that causes false positives and false negatives, thereby maintaining high detection accuracy and reliability.
Solution Approach 2:
The system automatically maintains rule specificity by self-adjusting to new application behaviors through continuous operational data analysis. This self-service maintenance eliminates the staleness problem associated with manual updates, ensuring rules remain precisely tailored to current application patterns without requiring human intervention.
4Productivity
If containerization is used for faster software development, then development efficiency improves, but inter-program communication constraints increase
Solution Approach 1:
The system uses universal security monitoring mechanisms that work across different container types and inter-program communication protocols. By implementing multi-functional monitoring that handles various containerization scenarios uniformly, the system maintains security effectiveness while accommodating the communication constraints imposed by containerization.
Data Source
AI summary
A computer-implemented method of monitoring applications executing on a plurality of computing nodes, includes the steps of: deploying agents on each of the computing nodes to detect operations performed on different objects by the executing applications; at each of the computing nodes, storing by a corresponding one of the agents, records of the operations performed on the different objects by the executing applications and associating each of the operations with a set of metadata properties; organizing the operations into groups based on the metadata properties; for one of the groups, generating a monitoring rule based on properties of the stored records of the operations of the one group; and configuring the agents according to the generated monitoring rule.