Graph Mixture Density Neural Network for Malicious Activity Detection
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Solution Overview
Problem
Current deep learning models struggle to effectively model and predict malicious network activity, particularly in graph-structured data, as they fail to incorporate inherent graph-based features, leading to inaccurate predictions and overlooking complex distributions in network behavior.
Innovation Solution
A graph mixture density neural network (GraphMDN) is employed to detect malicious network activity by generating and evaluating distributions of network activity data, capturing nuanced behaviors and anomalies within graph structures, rather than relying on hand-engineered features or averaging outputs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard deep learning models are used for network activity prediction, then the models can process data efficiently, but they fail to capture complex distributions and graph-structured features leading to inaccurate predictions
Solution Approach 1:
The patent combines Graph Neural Networks (GNN) which excel at processing graph-structured data with Mixture Density Networks (MDN) which model complex output distributions. This merging creates a hybrid architecture that simultaneously captures graph topology information and models multi-modal distributions of network activity metrics, resolving the contradiction between processing efficiency and predictive accuracy for complex network behaviors
Solution Approach 2:
The invention creates a composite model architecture integrating three distinct components: GNN layers for graph feature extraction, MDN for multi-modal distribution modeling, and attention mechanisms for feature weighting. This composite structure leverages the strengths of each component to achieve both accurate graph processing and complex distribution modeling, overcoming the limitations of individual approaches
2Ease of manufacture
If hand-engineered features are used to represent graph structure, then the implementation is simpler, but important information is lost and performance deteriorates
Solution Approach 1:
The GraphMDN model performs self-service by automatically learning optimal graph feature representations through the GNN component. Instead of relying on manual feature engineering, the network autonomously extracts meaningful structural patterns from the graph data during training, eliminating the need for hand-crafted features while capturing complex relationships that would be difficult to design manually
3Productivity
If aggregate data is used for analysis, then the computation is more efficient, but individual anomaly detection capability is reduced
Solution Approach 1:
The patent applies local quality by processing network traffic data at the individual flow level rather than aggregating immediately. The GNN processes each connection's graph representation independently, preserving local anomalies and fine-grained patterns. Aggregation occurs only at the decision stage, maintaining computational efficiency while detecting individual anomalies that would be obscured in fully aggregated data
Data Source
AI summary
Disclosed is an approach for detecting malicious network activity (e.g. based on a data hoarding activity identifies using a graph mixture density neural network (GraphMDN)). Generally, the approach includes generating embeddings using a graph convolution process and then processing the embeddings using a mixture density neural network. The approach may include collecting network activity data, generating a graph representing the network activity, or an aggregation thereof that maintains the inherent graphical nature and characteristics of the data, and training a GraphMDN in order to generate pluralities of distributions characterizing one or more aspects of the graph representing the network activity. The approach may also include capturing new network activity data, and evaluating that data using the distributions generated by the trained GraphMDN, and generation corresponding detection results.