Graph Database Network Anomaly Detection
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current computer network security systems face challenges in timely and effective detection of malicious threats due to the complexity of identifying anomalous network activities from vast amounts of log data, often resulting in delayed or missed cyber threats.
Innovation Solution
A scalable malicious threat detection system that utilizes graph database analysis and time-series analysis to process log data, generating network event graphs and applying graph queries to identify potential malicious activity by analyzing patterns of network activity, thereby enabling real-time detection and mitigation of security threats.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional log data analysis methods are used to detect malicious threats, then the system can process network logs, but the detection is delayed and less effective due to the complexity of identifying anomalous activities from vast amounts of data
Solution Approach 1:
The patent introduces graph databases as an intermediary layer between raw log data and detection algorithms. Log data is transformed into graph structures where nodes represent entities (IP addresses, users, devices) and edges represent relationships (communications, transactions). This intermediary representation enables efficient pattern matching and anomaly detection by leveraging graph traversal algorithms, resolving the contradiction between detection accuracy and time consumption
Solution Approach 2:
The patent transforms traditional flat log data into multi-dimensional graph structures, adding relational dimensions to the data. Instead of analyzing logs as sequential records, the system creates a graphical model with multiple dimensions (nodes, edges, paths, communities) that captures complex relationships. This dimensional transformation enables simultaneous analysis of multiple attack patterns and significantly improves both detection accuracy and speed
2Reliability
If comprehensive log data is collected from multiple sources to improve threat detection, then more security insights can be gained, but the data complexity and processing burden increase significantly
Solution Approach 1:
The patent creates a universal graph database schema that can accommodate multiple log sources (firewall logs, intrusion detection logs, authentication logs, application logs) through a unified node and edge structure. Different log types are represented as different node types or edge attributes within the same graph framework, enabling multi-functional analysis without increasing system complexity. This universal representation allows the system to process diverse log data while maintaining manageable complexity
Solution Approach 2:
The patent merges multiple log data streams into a single integrated graph structure. Instead of processing separate log files from different security devices independently, the system combines them into one graph where entities from different sources are connected through relationships. This merging consolidates data processing operations and enables cross-source correlation analysis, improving reliability while reducing the complexity of managing multiple separate processing pipelines
3Speed
If real-time parameter extraction is implemented to enable timely threat detection, then detection speed improves, but the processing load on the system increases
Solution Approach 1:
The patent performs preliminary actions by pre-processing log data into graph structures and pre-computing graph metrics (degree centrality, betweenness centrality, community detection) before actual threat detection queries. This preliminary transformation of raw logs into structured graph representations with pre-calculated attributes reduces the processing load during real-time detection, as the system only needs to perform graph traversals and pattern matching rather than full log parsing and analysis
Solution Approach 2:
The patent extracts only the essential parameters and relationships needed for threat detection from comprehensive log data, storing them as graph nodes and edges. Instead of processing and analyzing all log fields in real-time, the system extracts critical elements (source/destination IPs, timestamps, communication types, authentication outcomes) and stores them in the graph structure. This selective extraction reduces processing load while maintaining detection effectiveness by focusing on the most relevant security indicators
Data Source
AI summary
Graph database analysis for network anomaly detection systems, in which a data analysis device receives multiple log data entries including parameters associated with a computer network event in a computing network. The data analysis device extracts one or more parameters in real-time and generates a network event graph based on at least one of a first graph metric or a second graph metric. The first and second graph metrics are based on the one or more extracted parameters. The data analysis device detects, based on queries performed on the network event graph, at least one of an anomalous event associated with the computing network or a malicious event associated with the computing network.