Random CapsNet Forest Model for Imbalanced Malware Type Classification Task
This addresses the problem of complex and data-sensitive models in malware classification for cybersecurity applications, though it appears incremental as it builds on existing capsule network architectures.
The paper tackles malware type classification by proposing an ensemble capsule network model using bootstrap aggregating, achieving state-of-the-art results on two well-known malware datasets.
Behavior of a malware varies with respect to malware types. Therefore,knowing type of a malware affects strategies of system protection softwares. Many malware type classification models empowered by machine and deep learning achieve superior accuracies to predict malware types.Machine learning based models need to do heavy feature engineering and feature engineering is dominantly effecting performance of models.On the other hand, deep learning based models require less feature engineering than machine learning based models. However, traditional deep learning architectures and components cause very complex and data sensitive models. Capsule network architecture minimizes this complexity and data sensitivity unlike classical convolutional neural network architectures. This paper proposes an ensemble capsule network model based on bootstrap aggregating technique. The proposed method are tested on two malware datasets, whose the-state-of-the-art results are well-known.