Malware Classification
37 papers with code • 2 benchmarks • 5 datasets
Malware Classification is the process of assigning a malware sample to a specific malware family. Malware within a family shares similar properties that can be used to create signatures for detection and classification. Signatures can be categorized as static or dynamic based on how they are extracted. A static signature can be based on a byte-code sequence, binary assembly instruction, or an imported Dynamic Link Library (DLL). Dynamic signatures can be based on file system activities, terminal commands, network communications, or function and system call sequences.
Source: Behavioral Malware Classification using Convolutional Recurrent Neural Networks
Benchmarks
These leaderboards are used to track progress in Malware Classification
EMBER
Malimg
IoT-23
Subtasks
Most implemented papers
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
This paradigm is presented and discussed in the present paper, where emphasis has been given to the phases related to the extraction, and selection of a set of novel features for the effective representation of malware samples.
A New Burrows Wheeler Transform Markov Distance
MaksimEkin/COVID19-Literature-Clustering
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Prior work inspired by compression algorithms has described how the Burrows Wheeler Transform can be used to create a distance measure for bioinformatics problems.
Learning a Neural-network-based Representation for Open Set Recognition
shrtCKT/opennet
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Open set recognition problems exist in many domains.
Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers
Training pipelines for machine learning (ML) based malware classification often rely on crowdsourced threat feeds, exposing a natural attack injection point.
Convolutional Neural Network for Classification of Malware Assembly Code
danielgibert/mlw_classification_cnn_assembly
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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.
Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine (SVM) for Malware Classification
AFAgarap/malware-classification
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We envision an intelligent anti-malware system that utilizes the power of deep learning (DL) models.
Classification of Malware by Using Structural Entropy on Convolutional Neural Networks
danielgibert/mlw_classification_structural_entropy
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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.
Robust Neural Malware Detection Models for Emulation Sequence Learning
These models target the core of the malicious operation by learning the presence and pattern of co-occurrence of malicious event actions from within these sequences.
Deep learning at the shallow end: Malware classification for non-domain experts
ceadarireland/deeplearningattheshallowend
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Current malware detection and classification approaches generally rely on time consuming and knowledge intensive processes to extract patterns (signatures) and behaviors from malware, which are then used for identification.
Using Convolutional Neural Networks for Classification of Malware represented as Images
danielgibert/mlw_classification_cnn_img
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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.
