Graph Convolutional Memory Network for Interactive Anomaly Detection
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Solution Overview
Problem
Current anomaly detection methods in intrusion detection systems face challenges with scalability and representation learning, particularly in preserving the interactive graph structure, leading to suboptimal results and high computational complexity.
Innovation Solution
A graph convolutional memory network is developed to learn interaction patterns between components in attributed bipartite graphs, using a novel loss function that enables unsupervised learning and effectively detects anomalies by preserving the interactive structure of the graph.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If graph embedding techniques are used to preserve interactive graph structure, then detection accuracy is improved, but computational complexity increases
Solution Approach 1:
The patent replaces traditional graph embedding techniques with a graph convolutional memory network that uses message-passing mechanisms and attention-based feature aggregation. This substitution maintains the ability to preserve interactive graph structure while reducing computational complexity by avoiding the expensive matrix factorization operations typical of traditional embedding methods.
Solution Approach 2:
The patent segments the graph processing into localized neighborhood operations through the graph convolutional network, where each node processes information from its immediate neighbors rather than computing global graph embeddings. This segmentation reduces computational complexity from O(n²) or O(n³) to O(n) or O(n log n) while preserving local interaction patterns essential for anomaly detection.
2Loss of information
If traditional graph analytics features are computed, then representation of underlying structure is achieved, but scalability deteriorates
Solution Approach 1:
The patent implements dynamic feature computation where graph convolutional filters are applied adaptively based on local graph structure and node attributes. Instead of computing static global features, the system dynamically aggregates features from variable-sized neighborhoods, enabling scalable processing that adapts to different graph densities and sizes while maintaining comprehensive structure representation.
Solution Approach 2:
The patent transforms the graph structure into a different dimensional representation through convolutional operations in the graph domain, converting complex high-dimensional graph relationships into lower-dimensional node embeddings that retain essential structural information. This dimensional transformation enables efficient processing while preserving the underlying graph structure.
3Measurement precision
If supervised anomaly detection methods are used, then detection precision is improved, but adaptability to new threats deteriorates
Solution Approach 1:
The patent implements self-service learning through the graph convolutional memory network, where the system automatically learns normal behavior patterns from unlabeled graph data and uses these learned patterns to detect anomalies. The attention mechanisms enable the system to self-adapt to new threat patterns by dynamically adjusting feature weights based on observed interactions, eliminating the need for continuous supervised retraining while maintaining high detection precision.
Data Source
AI summary
A system and method for detecting anomaly behavior in interactive networks are described. An attributed bipartite graph related problem is generated. A graph convolutional memory network is developed based on the generated problem. A loss function is further developed based on the developed graph convolutional memory network. The developed graph convolutional memory network is trained to learn interaction patterns between different components. Anomalies are detected based on the trained developed graph convolutional memory network.