Malware Classification

33 papers with code • 2 benchmarks • 2 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

Most implemented papers

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.

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.

Learning a Neural-network-based Representation for Open Set Recognition

shrtCKT/opennet 12 Feb 2018

Open set recognition problems exist in many domains.

Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers

ClonedOne/MalwareBackdoors 2 Mar 2020

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 27 Oct 2017

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.

Classification of Malware by Using Structural Entropy on Convolutional Neural Networks

danielgibert/mlw_classification_structural_entropy 27 Apr 2018

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

tychen5/sportslottery 28 Jun 2018

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 22 Jul 2018

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 27 Aug 2018

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.