Counteracting Concept Drift by Learning with Future Malware Predictions

arXiv:2404.09352v1 Announce Type: new
Abstract: The accuracy of deployed malware-detection classifiers degrades over time due to changes in data distributions and increasing discrepancies between training and testing data. This phenomenon is known as the concept drift. While the concept drift can be caused by various reasons in general, new malicious files are created by malware authors with a clear intention of avoiding detection. The existence of the intention opens a possibility for predicting such future samples. Including predicted samples in …

accuracy arxiv can concept cs.ai cs.cr data detection distributions files future general malicious malware predictions testing training