Malware Classification
41 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
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.
Applications of Graph Integration to Function Comparison and Malware Classification
The result is a fast, intuitive, and easy-to-compute glass-box vectorization scheme, which can be leveraged for training a standalone classifier or to augment an existing feature space.
Deep-Net: Deep Neural Network for Cyber Security Use Cases
In this paper, we attempt to apply DNNs on three different cyber security use cases: Android malware classification, incident detection and fraud detection.
Deep Transfer Learning for Static Malware Classification
In the transfer learning scheme, we borrow knowledge from natural images or objects and apply to the target domain of static malware detection.
Transfer Learning for Image-Based Malware Classification
In this paper, we consider the problem of malware detection and classification based on image analysis.
Activation Analysis of a Byte-Based Deep Neural Network for Malware Classification
Feature engineering is one of the most costly aspects of developing effective machine learning models, and that cost is even greater in specialized problem domains, like malware classification, where expert skills are necessary to identify useful features.
KiloGrams: Very Large N-Grams for Malware Classification
N-grams have been a common tool for information retrieval and machine learning applications for decades.
A Convolutional Transformation Network for Malware Classification
In this paper, we introduce a novel approach to classify malware by using a deep network on images transformed from binary samples.
Dynamic data fusion using multi-input models for malware classification
viktorZenkov/MalwareClassification
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To solve this, we investigated four cases: a text-only model, a hexadecimal-only model, a multi-input model using both text and hexadecimal inputs, and a model based on combining the individual results.
Malware Classification using Deep Learning based Feature Extraction and Wrapper based Feature Selection Technique
cyberhunters/Malware-Detection-Using-Machine-Learning
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The proposed DLMD technique uses both the byte and ASM files for feature engineering, thus classifying malware families.
