Context-Based Rule Setup for Security Situation Detection
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Solution Overview
Problem
Conventional security monitoring platforms require complex and time-consuming processes for configuring rules to identify abnormal situations, leading to sub-optimal rules that can result in false negatives or false positives, and are difficult to navigate, requiring skilled operators.
Innovation Solution
A computer-implemented method that allows users to quickly set up new rules by selecting contextual data in a graphical user interface, using metadata from various sources to define conditions for identifying abnormal situations, thereby reducing the need for complex navigation and operator skill.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If operators manually configure rules in conventional security monitoring platforms, then the system can detect abnormal situations, but the process is time-consuming and requires substantial skill and experience
Solution Approach 1:
The system automatically generates rules by extracting contextual information from event data and presenting it through a simplified user interface. Operators simply select relevant context elements rather than manually configuring complex rule parameters, enabling the system to serve itself in rule generation while requiring minimal operator intervention.
Solution Approach 2:
The system pre-processes event data to extract and organize contextual information before the operator needs to configure rules. By preparing the contextual data structure in advance and presenting it through the user interface, the system eliminates the need for operators to navigate complex hierarchies and search for information during rule configuration.
2Reliability
If operators manually configure rules in conventional security monitoring platforms, then the system can detect abnormal situations, but the interface becomes complex and requires substantial skill to navigate
Solution Approach 1:
The user interface is segmented into distinct functional areas: event data display, contextual information extraction, and rule configuration. By dividing the complex rule setup process into these manageable segments, the system presents operators with simplified interactions rather than requiring navigation through complex hierarchical windows.
Solution Approach 2:
The system introduces an intermediary layer that automatically extracts contextual information from raw event data and presents it through a simplified user interface. This intermediary process translates complex event data into structured contextual elements that operators can easily select to generate rules, eliminating the need to navigate complex interfaces.
3Reliability
If operators manually configure rules in conventional security monitoring platforms, then the system can detect abnormal situations, but the rules may be sub-optimal due to operator limitations
Solution Approach 1:
The system presents extracted contextual information from event data to the operator through the user interface, allowing operators to review and select relevant elements. This feedback mechanism ensures that the generated rules are based on actual observed patterns rather than operator assumptions, improving rule accuracy while reducing the skill required.
Solution Approach 2:
The system replaces the mechanical process of manual rule configuration with an automated information extraction and presentation mechanism. By using computational methods to analyze event data and generate contextual information, the system eliminates the limitations of operator experience and knowledge while maintaining the ability to detect abnormal situations.
4Ease of operation
If conventional security monitoring platforms use preconfigured rules, then the interface remains simple, but new abnormal situations cannot be detected without configuring new rules
Solution Approach 1:
The system dynamically adapts to new abnormal situations by extracting contextual information from observed events and automatically generating updated rules. Rather than requiring a static set of preconfigured rules, the system continuously evolves its rule set based on actual event data, maintaining interface simplicity while improving adaptability.
Solution Approach 2:
The system serves itself by automatically extracting contextual information from event data and generating rules for new abnormal situations. When operators encounter new patterns, the system captures the contextual information and creates appropriate rules without requiring manual reconfiguration, thus maintaining interface simplicity while enhancing versatility.
Data Source
AI summary
A computer-implemented method, a device, and a computer readable storage medium are disclosed to enable the definition of rules for identifying occurrences of situations in a security system. The method is performed at an electronic device with access to a user interface. The method includes: obtaining indicia; accessing one or more information sources to retrieve contextual information based on the indicia; exposing, via the user interface, one or more condition-related user-selectable user interface elements; generating a rule for identifying occurrences of a situation based on one or more respective conditions corresponding to a selected subset of the one or more condition-related user-selectable user interface elements; and storing the rule in a rule database for use in monitoring for occurrences of the situation.