Malware Classification

26 papers with code • 2 benchmarks • 1 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

Datasets


Greatest papers with code

Integration of Static and Dynamic Analysis for Malware Family Classification with Composite Neural Network

guelfoweb/peframe 24 Dec 2019

In this paper, we combine static and dynamic analysis features with deep neural networks for Windows malware classification.

General Classification Malware Classification

A New Burrows Wheeler Transform Markov Distance

MaksimEkin/COVID19-Literature-Clustering 30 Dec 2019

Prior work inspired by compression algorithms has described how the Burrows Wheeler Transform can be used to create a distance measure for bioinformatics problems.

Malware Classification

Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification

ManSoSec/Microsoft-Malware-Challenge 13 Nov 2015

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.

General Classification Malware Classification

Transfer Learning for Image-Based Malware Classification

pratikpv/malware_classification 21 Jan 2019

In this paper, we consider the problem of malware detection and classification based on image analysis.

General Classification Malware Detection +1

An Empirical Analysis of Image-Based Learning Techniques for Malware Classification

pratikpv/malware_detect2 24 Mar 2021

In this paper, we consider malware classification using deep learning techniques and image-based features.

General Classification Malware Classification +1

A Framework for Enhancing Deep Neural Networks Against Adversarial Malware

deqangss/aics2019_challenge_adv_mal_defense 15 Apr 2020

By conducting experiments with the Drebin Android malware dataset, we show that the framework can achieve a 98. 49\% accuracy (on average) against grey-box attacks, where the attacker knows some information about the defense and the defender knows some information about the attack, and an 89. 14% accuracy (on average) against the more capable white-box attacks, where the attacker knows everything about the defense and the defender knows some information about the attack.

General Classification Malware Detection

KiloGrams: Very Large N-Grams for Malware Classification

NeuromorphicComputationResearchProgram/KiloGrams 1 Aug 2019

N-grams have been a common tool for information retrieval and machine learning applications for decades.

General Classification Information Retrieval +1