Graph Database Anomaly Detection for Information Security
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Solution Overview
Problem
Current methods for identifying anomalies in information systems rely on statistical analysis, which can be vulnerable to attackers masking their actions as normal behavior, making it difficult to effectively detect and respond to security threats.
Innovation Solution
A system that collects and stores information about the hierarchy of capabilities and resources in a graph database, defining edges to represent relationships, and compares event and state data to this database to identify anomalies, allowing for real-time security actions to be taken.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If statistical analysis methods are used to identify anomalies, then the system can detect deviations from normal behavior, but attackers can mask their actions as normal behavior and evade detection
Solution Approach 1:
The system segments the anomaly detection process into multiple independent analysis components: graph database structure analysis, capability hierarchy verification, resource relationship validation, and event pattern matching. Each segment analyzes specific aspects of system behavior independently, then combines results to make comprehensive anomaly determination, preventing attackers from evading detection by manipulating single statistical parameters
Solution Approach 2:
The patent introduces a graph database as an intermediary layer between raw system events and anomaly detection. The graph database stores predefined capability hierarchies, resource relationships, and normal behavior patterns as structured knowledge. This intermediary enables the system to compare actual system behavior against established behavioral models rather than relying solely on statistical deviations, making it harder for attackers to mask malicious actions as normal behavior
2Measurement precision
If a graph database with detailed capability and resource hierarchies is maintained, then the system can accurately identify anomalies by comparing against expected behavior, but the system complexity increases
Solution Approach 1:
The graph database structure serves multiple functions simultaneously: it stores capability hierarchies for authorization verification, maintains resource relationships for dependency analysis, defines normal behavior patterns for anomaly detection, and provides context for event correlation. This multi-functionality reduces the need for separate data structures for each purpose, managing complexity while enabling comprehensive security analysis
Solution Approach 2:
The system implements a nested hierarchical structure where capabilities contain resources, which contain specific instances. This nested organization allows the graph database to represent complex system relationships in a structured manner, where each level of hierarchy can be analyzed independently or in combination with other levels, making the complex data structure more manageable and query-efficient
3Speed
If real-time comparison of system events against the graph database is performed, then timely security actions can be taken, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing capability hierarchies, resource relationships, and normal behavior patterns in the graph database during system setup and operation. This pre-structuring of knowledge enables real-time event comparison to focus only on matching actual system events against predefined patterns, rather than performing complex analysis from scratch, reducing real-time computational energy consumption while maintaining fast anomaly detection
Solution Approach 2:
The anomaly detection system dynamically adjusts its analysis depth and scope based on event characteristics. For routine events, the system performs quick pattern matching against the graph database. For more complex or suspicious events, the system dynamically expands the analysis to include deeper hierarchy verification and broader relationship checking, optimizing the balance between detection accuracy and computational energy consumption in real-time
Data Source
AI summary
A system for identifying anomalies in an information system is typically configured for: collecting information regarding a hierarchy of capabilities, a hierarchy of resources, capability instances, and resource instances of the information system; storing, in a graph database, nodes corresponding to the hierarchy of capabilities, hierarchy of resources, capability instances, and resource instances; collecting information regarding relationships among the hierarchy of capabilities, hierarchy of resources, capability instances, and resource instances; defining, in the graph database, edges corresponding to the relationships among the hierarchy of capabilities, hierarchy of resources, capability instances, and resource instances; collecting event and/or state data for the information system; comparing the event and/or state data to the graph database and determining that an event and/or state is anomalous; and, in response to determining that the event and/or state is anomalous, taking an information security action.