Graph Deviation Networks for Few-Shot Anomaly Detection
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Solution Overview
Problem
Current network anomaly detection methods face challenges in accurately identifying anomalies with limited labeled data, as they often require large percentages of positive examples and can lead to unstable results due to negative transfer when knowledge is transferred from a single network, especially when anomalies exist in different manifolds across networks.
Innovation Solution
The Graph Deviation Networks (GDN) employ a cross-network meta-learning algorithm to leverage few-shot labeled anomalies, using a GNN-based anomaly score learner and deviation loss to distinguish normal and abnormal nodes, and the Meta-GDN framework transfers meta-knowledge from multiple auxiliary networks to improve anomaly detection performance on a target network.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing semi-supervised or PU learning techniques are adopted for anomaly detection, then the model can leverage labeled anomalies, but it still requires a relatively large percentage of positive examples and fails when only few-shot labeled data is available
Solution Approach 1:
The system performs preliminary action by pre-training the GNN model on multiple auxiliary networks to learn general anomaly detection patterns before adapting to the target network. This meta-learning approach allows the model to acquire transferable knowledge from auxiliary networks, reducing the dependency on large amounts of labeled data in the target network. The model learns to recognize anomaly patterns across different network structures and domains, enabling effective few-shot detection on the target network.
2Adaptability or versatility
If knowledge is transferred from a single network to the target network, then the model can leverage source network knowledge, but the results become unstable and suffer from negative transfer when anomalies exist in different manifolds
Solution Approach 1:
The system merges knowledge from multiple auxiliary networks by aggregating their learned representations and anomaly detection patterns. Instead of transferring knowledge from a single source network, the model integrates information from multiple networks with different anomaly manifolds, creating a more robust and generalizable anomaly detection capability. This combination approach balances the knowledge from different sources, reducing the risk of negative transfer and improving detection stability on the target network.
Solution Approach 2:
The system achieves universality by designing a meta-learning framework that can adapt to multiple different anomaly detection tasks across various networks. The GNN model learns universal anomaly detection patterns that are applicable across different network domains and anomaly types. This multi-functional capability allows the same model to effectively detect anomalies in diverse networks without requiring task-specific customization, thereby improving reliability across different scenarios.
3Difficulty of detecting and measuring
If autoencoder-based models or matrix factorization methods are used, then the model can identify anomalies through reconstruction errors, but the identified anomalies may be data noises or uninteresting instances due to lack of prior knowledge
Solution Approach 1:
The system implements feedback by incorporating labeled anomaly information from auxiliary networks into the training process. The model uses these labeled examples to guide the learning of anomaly detection patterns, providing feedback signals that help the model distinguish between true anomalies and data noises. This supervised feedback mechanism enables the model to learn meaningful anomaly characteristics rather than simply detecting reconstruction errors, thereby reducing false positives and improving detection quality.
Solution Approach 2:
The system applies parameter changes by learning domain-specific anomaly detection parameters from auxiliary networks that can be adapted to the target network. Instead of using generic reconstruction error thresholds, the model learns task-specific parameters and representations that capture the unique characteristics of anomalies in different domains. This parameter adaptation allows the model to maintain sensitivity to true anomalies while filtering out noise, effectively preserving and utilizing prior knowledge about anomaly patterns.
Data Source
AI summary
Various embodiments for few-shot network anomaly detection via cross-network meta-learning are disclosed herein. An anomaly detection system incorporating a new family of graph neural networks—Graph Deviation Networks (GDN) can leverage a small number of labeled anomalies for enforcing statistically significant deviations between abnormal and normal nodes on a network. Further, the GDN is equipped with a new cross-network meta-learning algorithm (Meta-GDN) to realize few-shot network anomaly detection by transferring meta-knowledge from multiple auxiliary networks. Extensive evaluations demonstrate the efficacy of the anomaly detection system and the Meta-GDN on few-shot or even one-shot network anomaly detection.