Random CapsNet Forest Model for Imbalanced Malware Type Classification Task

TL;DR

This paper introduces an ensemble capsule network model using bootstrap aggregating for classifying malware types, aiming to improve accuracy and reduce data sensitivity compared to traditional deep learning methods.

Contribution

It presents a novel ensemble capsule network approach that minimizes complexity and data sensitivity for malware classification, outperforming existing models.

Findings

Achieved superior accuracy on two malware datasets.

Reduced model complexity and data sensitivity.

Outperformed state-of-the-art malware classification methods.

Abstract

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.