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Found 9 papers, 6 papers with code
A Robust Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via (De)Randomized Smoothing
Our findings reveal that the chunk-based smoothing classifiers exhibit greater resilience against adversarial malware examples generated with state-of-the-are evasion attacks, outperforming a non-smoothed classifier and a randomized smoothing-based classifier by a great margin.
Towards a Practical Defense against Adversarial Attacks on Deep Learning-based Malware Detectors via Randomized Smoothing
Malware detectors based on deep learning (DL) have been shown to be susceptible to malware examples that have been deliberately manipulated in order to evade detection, a. k. a.
Orthrus: A Bimodal Learning Architecture for Malware Classification
Malware detection and classification is a challenging problem and an active area of research.
HYDRA: A multimodal deep learning framework for malware classification
While traditional machine learning methods for malware detection largely depend on hand-designed features, which are based on experts’ knowledge of the domain, end-to-end learning approaches take the raw executable as input, and try to learn a set of descriptive features from it.
A Hierarchical Convolutional Neural Network for Malware Classification
Novel approaches in the literature treat an executable as a sequence of bytes or as a sequence of assembly language instructions.
An End-to-End Deep Learning Architecture for Classification of Malware’s Binary Content
In traditional machine learning techniques for malware detection and classification, significant efforts are expended on manually designing features based on expertise and domain-specific knowledge.
Using Convolutional Neural Networks for Classification of Malware represented as Images
This means that malicious files belonging to the same family, with the same malicious behavior, are constantly modified or obfuscated using several techniques, in such a way that they look like different files.
Ranked #1 on
Malware Classification
on Malimg Dataset
Classification of Malware by Using Structural Entropy on Convolutional Neural Networks
Motivated by the visual similarity between streams of entropy of malicious software belonging to the same family, we propose a file agnostic deep learning approach for categorization of malware.
Ranked #5 on
Malware Classification
on Microsoft Malware Classification Challenge
(LogLoss metric)
Convolutional Neural Network for Classification of Malware Assembly Code
Traditional signature-based methods have started becoming inadequnate to deal with next generation malware which utilize sophisticated obfuscation (polymorphic and metamorphic) techniques to evade detection.
