Synthetic Traffic Training for Sparse Network Anomaly Detection

The patent transforms the detection approach by changing from fixed heuristic rules to dynamic machine learning models with adjustable parameters. The system uses synthetic data generation to create diverse training samples, allowing the model to adapt its detection parameters for both known and unknown attack patterns. This parameter transformation enables the system to maintain high accuracy for known attacks while gaining versatility against novel threats.

The patent applies preliminary action by generating synthetic training data before deployment. The system pre-generates diverse attack scenarios including stealthy attacks and new vulnerability exploits, then trains the anomaly detection model on this prepared dataset. This preliminary training enables the model to recognize patterns of unknown threats before they appear in real network traffic, resolving the contradiction between detecting known attacks and adapting to new threats.

The patent applies partial action by using synthetic data to supplement rather than replace real training data. The system generates synthetic anomalous patterns at various densities and combines them with real network traffic data in controlled proportions. This partial use of synthetic data allows the model to learn sensitivity to sparse patterns while being grounded in real traffic characteristics, thereby improving detection capability without excessively increasing false positives.

The patent implements feedback mechanisms through iterative model training and evaluation. The system trains the anomaly detection model, evaluates its performance on both known attacks and normal traffic, then uses the results to refine synthetic data generation and model parameters. This feedback loop allows the system to optimize detection sensitivity while monitoring and controlling false positive rates, resolving the contradiction between detecting sparse patterns and maintaining reliability.

The patent directly applies copying by generating synthetic copies of anomalous network traffic patterns. Instead of requiring additional real attack data, the system creates synthetic replicas of various attack types including stealthy attacks and exploits of new vulnerabilities. These synthetic copies preserve the essential characteristics of real attacks while eliminating privacy and data availability constraints, allowing extensive model training without needing more real-world compromised data.

The patent uses synthetic data as an intermediary between the need for training data and the constraints of data availability and privacy. The synthetic data generation system acts as a mediator that translates limited real attack samples into diverse training datasets. This intermediary approach allows the model to achieve high training accuracy without direct access to large quantities of real anomalous traffic, resolving the contradiction between model accuracy and data availability.