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Daniel Gibert
Researcher at University of Lleida
Publications - 14
Citations - 625
Daniel Gibert is an academic researcher from University of Lleida. The author has contributed to research in topics: Malware & Deep learning. The author has an hindex of 7, co-authored 10 publications receiving 226 citations.
Papers
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Journal ArticleDOI
The rise of machine learning for detection and classification of malware: Research developments, trends and challenges
TL;DR: This survey aims at providing a systematic and detailed overview of machine learning techniques for malware detection and in particular, deep learning techniques with special emphasis on deep learning approaches.
Journal ArticleDOI
Using convolutional neural networks for classification of malware represented as images
TL;DR: Motivated by the visual similarity between malware samples of the same family, a file agnostic deep learning approach is proposed to efficiently group malicious software into families based on a set of discriminant patterns extracted from their visualization as images.
Journal ArticleDOI
HYDRA: A multimodal deep learning framework for malware classification
TL;DR: HYDRA is presented, a novel framework to address the task of malware detection and classification by combining various types of features to discover the relationships between distinct modalities and achieves comparable results to gradient boosting methods in the literature and higher yield in comparison with deep learning approaches.
Proceedings Article
Classification of Malware by Using Structural Entropy on Convolutional Neural Networks
TL;DR: This paper proposes a file agnostic deep learning approach for categorization of malware that exploits the fact that most variants are generated by using common obfuscation techniques and that compression and encryption algorithms retain some properties present in the original code.
Proceedings ArticleDOI
A Hierarchical Convolutional Neural Network for Malware Classification
TL;DR: A Hierarchical Convolutional Network (HCN) is proposed for malware classification that has two levels of convolutional blocks applied at the mnemonic-level and at the function-level, enabling us to extract n-gram like features from both levels when constructing the malware representation.