Graph Neural Network Anomaly Detection via Adversarial Training
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Solution Overview
Problem
Existing anomaly detection systems in computer networks are vulnerable to adversarial attacks, where intelligent attackers can create adversarial samples that evade detection by adapting the behavior of machine learning systems.
Innovation Solution
The method involves generating an adversarial training data set using integrated gradient attacks to create adversarial samples, encoding both original and adversarial samples into graph representations, and training a graph-based neural network to detect anomalous activity in computer networks, thereby enhancing the robustness of the detection system.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional anomaly detection systems are used, then detection speed is maintained, but the system becomes vulnerable to adversarial attacks
Solution Approach 1:
The system performs preliminary adversarial training by generating adversarial samples and training the model before actual deployment. This preliminary action prepares the model to resist future adversarial attacks, resolving the contradiction by establishing robustness in advance while managing training complexity through automated gradient-based generation.
Solution Approach 2:
The system converts harmful adversarial samples into beneficial training data. By using gradient-based attacks to generate adversarial examples and incorporating them into the training set, the system transforms potential threats into opportunities for improving model robustness, thereby resolving the contradiction between reliability and complexity.
2Reliability
If adversarial training is implemented, then resistance to attacks is improved, but training time and computational resources increase
Solution Approach 1:
The system changes training parameters by incorporating adversarial samples with controlled perturbation magnitudes (e.g., L-infinity norm constraints). This allows the model to learn robust features while limiting the computational burden of generating and processing highly complex adversarial examples, thus resolving the contradiction between adversarial resistance and training time.
3Measurement precision
If graph-based neural networks are used, then detection accuracy is improved, but system complexity increases
Solution Approach 1:
The system uses graph representations as an intermediary structure to capture relationships between system components. By encoding data as graphs with nodes and edges representing entities and their relationships, the model achieves higher detection accuracy while the graph structure provides a natural and interpretable framework that manages architectural complexity.
Data Source
AI summary
Methods and systems for detecting and responding to an intrusion in a computer network include generating an adversarial training data set that includes original samples and adversarial samples, by perturbing one or more of the original samples with an integrated gradient attack to generate the adversarial samples. The original and adversarial samples are encoded to generate respective original and adversarial graph representations, based on node neighborhood aggregation. A graph-based neural network is trained to detect anomalous activity in a computer network, using the adversarial training data set. A security action is performed responsive to the detected anomalous activity.