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.

中文翻译：

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用于不平衡恶意软件类型分类任务的随机 CapsNet 森林模型

恶意软件的行为因恶意软件类型而异。因此，了解恶意软件的类型会影响系统保护软件的策略。许多由机器和深度学习赋能的恶意软件类型分类模型在预测恶意软件类型方面具有较高的准确性。基于机器学习的模型需要进行大量的特征工程，而特征工程主要影响模型的性能。另一方面，基于深度学习的模型需要较少特征工程而不是基于机器学习的模型。然而，传统的深度学习架构和组件会导致非常复杂和数据敏感的模型。与经典的卷积神经网络架构不同，胶囊网络架构最大限度地减少了这种复杂性和数据敏感性。本文提出了一种基于bootstrap聚合技术的集成胶囊网络模型。所提出的方法在两个恶意软件数据集上进行了测试，其最先进的结果是众所周知的。