Multifamily Malware Models
This work addresses the efficiency of using a single model for detecting multiple malware families, though it appears incremental as it builds on prior research on dataset diversity.
The study tackled the trade-off between accuracy and dataset diversity in malware detection by training models on multiple families, finding that neighborhood-based algorithms generalized well and outperformed other techniques.
When training a machine learning model, there is likely to be a tradeoff between accuracy and the diversity of the dataset. Previous research has shown that if we train a model to detect one specific malware family, we generally obtain stronger results as compared to a case where we train a single model on multiple diverse families. However, during the detection phase, it would be more efficient to have a single model that can reliably detect multiple families, rather than having to score each sample against multiple models. In this research, we conduct experiments based on byte $n$-gram features to quantify the relationship between the generality of the training dataset and the accuracy of the corresponding machine learning models, all within the context of the malware detection problem. We find that neighborhood-based algorithms generalize surprisingly well, far outperforming the other machine learning techniques considered.