Random CapsNet Forest Model for Imbalanced Malware Type Classification Task

2019-12-20 · Aykut Çayır, Uğur Ünal, Hasan Dağ

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.

📄 PDF Abstract BibTeX arXiv:1912.10836

Code (0)

등록된 구현이 없습니다.

Tasks

ClassificationDeep LearningFeature Engineering General Classification Vocal Bursts Type Prediction

Methods 이 논문이 사용한 방법론

Capsule Network A capsule is an activation vector that basically executes on its inputs some complex internal computations. Length of these activation vectors signifies the probability of…

CardioCaps: Attention-based Capsule Network for Class-Imbalanced Echocardiogram Classification

2024-03-14 · Hyunkyung Han, Jihyeon Seong, Jaesik Choi

Capsule Neural Networks (CapsNets) is a novel architecture that utilizes vector-wise representations formed by multiple neurons. Specifically, the Dynamic Routing CapsNets (DR-CapsNets) employ an affine matrix and dynami…

image-classification Image Classification regression

Dynamic detection of mobile malware using smartphone data and machine learning

2021-07-23 · J. S. Panman de Wit, J. van der Ham, D. Bucur

Mobile malware are malicious programs that target mobile devices. They are an increasing problem, as seen in the rise of detected mobile malware samples per year. The number of active smartphone users is expected to grow…

BIG-bench Machine Learning CPU Malware Detection

An Ensemble of Pre-trained Transformer Models For Imbalanced Multiclass Malware Classification

2021-12-25 · Ferhat Demirkıran, Aykut Çayır, Uğur Ünal, Hasan Dağ

Classification of malware families is crucial for a comprehensive understanding of how they can infect devices, computers, or systems. Thus, malware identification enables security researchers and incident responders to …

Malware Classification

Random Forest for Malware Classification

2016-09-25 · Felan Carlo C. Garcia, Felix P. Muga II

The challenge in engaging malware activities involves the correct identification and classification of different malware variants. Various malwares incorporate code obfuscation methods that alters their code signatures e…

Classification General Classification Malware Classification

Imbalanced malware classification: an approach based on dynamic classifier selection

2025-03-30 · J. V. S. Souza, C. B. Vieira, G. D. C. Cavalcanti, R. M. O. Cruz

In recent years, the rise of cyber threats has emphasized the need for robust malware detection systems, especially on mobile devices. Malware, which targets vulnerabilities in devices and user data, represents a substan…

Malware Classification Malware Detection