Generative Malware Prediction for Concept Drift Detection

The patent applies preliminary action by training generative models on historical malware data before new threats emerge. These models predict future malware characteristics in advance, allowing the detection system to be pre-prepared for evolving threats rather than reacting after they appear. This proactive approach bridges the gap between static detection methods and dynamic threat evolution.

The patent uses copying by creating synthetic malware samples through generative models that replicate the characteristics and evolution patterns of real malware. These generated copies serve as training data for detection algorithms, enabling them to learn from predicted future malware variants without requiring actual future threat data. This copying mechanism allows the system to adapt to evolving threats while maintaining reliability on known malware patterns.

The patent implements self-service by enabling the malware detection system to automatically generate its own training data through generative models. Instead of requiring manual updates with new malware samples from security researchers, the system autonomously predicts and generates future malware variants for self-training. This self-service mechanism reduces external dependency and simplifies the update process while maintaining high adaptability.

The patent applies parameter changes by transforming the detection problem from requiring frequent updates of detection rules to using a static generative model that predicts parameter evolution of malware. The generative model learns temporal patterns in malware characteristics once, then generates predictions for future states without requiring retraining. This changes the system from dynamically updated parameters to statically learned prediction parameters, reducing complexity.

The patent applies preliminary action by performing comprehensive malware analysis in advance during the offline training phase. The generative models are trained on historical malware data to learn evolution patterns before deployment. Once trained, these models can rapidly generate predictions for future malware without requiring repeated comprehensive analyses, thus reducing online processing time while maintaining high characterization accuracy.

The patent implements periodic action by conducting comprehensive malware analysis periodically during model training phases rather than continuously during operation. The generative models capture temporal evolution patterns at specific intervals, then use these learned patterns to efficiently generate predictions between training periods. This periodic comprehensive analysis reduces overall computational cost compared to continuous analysis while maintaining detection accuracy.