Malware Classification

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

Papers

Recently Added Most Hyped Most Active Needs Verification Most Verified

Showing 1–10 of 146 papers

Title	Date	Tasks	Status	Hype	Score
EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers	Jun 5, 2025	Malware AnalysisMalware Classification	CodeCode Available	2	5
A New Burrows Wheeler Transform Markov Distance	Dec 30, 2019	ClusteringMalware Classification	CodeCode Available	1	5
Assemblage: Automatic Binary Dataset Construction for Machine Learning	May 7, 2024	Malware Classification	CodeCode Available	1	5
A Comprehensive Study on Learning-Based PE Malware Family Classification Methods	Oct 29, 2021	ClassificationMalware Classification	CodeCode Available	1	5
Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performance	Jul 27, 2023	ClassificationMalware Classification	CodeCode Available	1	5
Deep Learning for Android Malware Defenses: a Systematic Literature Review	Mar 9, 2021	Android Malware DetectionDeep Learning	CodeCode Available	1	5
An Empirical Analysis of Image-Based Learning Techniques for Malware Classification	Mar 24, 2021	Deep LearningGeneral Classification	CodeCode Available	1	5
An Ensemble of Pre-trained Transformer Models For Imbalanced Multiclass Malware Classification	Dec 25, 2021	Malware Classification	CodeCode Available	1	5
A Dynamic Weighted Federated Learning for Android Malware Classification	Nov 23, 2022	ClassificationFederated Learning	CodeCode Available	1	5
A Framework for Enhancing Deep Neural Networks Against Adversarial Malware	Apr 15, 2020	General ClassificationMalware Classification	CodeCode Available	1	5

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All datasets Microsoft Malware Classification Challenge Malimg Dataset MaleVis

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	MalConv	Accuracy (10-fold)	9,641	—	Unverified
2	TPOT Classifier	Accuracy (5-fold)	98.94	—	Unverified
3	CNN BiLSTM - Reb Sampl	Accuracy (5-fold)	98.2	—	Unverified
4	Ahmadi et al. (2016): ENT, Bytes 1-G, STR, IMG1, IMG2, MD1, MISC, OPC, SEC, REG, DP, API, SYM, MD2 IMG and Opcode N-Grams + Ensemble Learning (XGBoost)	Accuracy (10-fold)	1	—	Unverified
5	HYDRA	Accuracy (10-fold)	1	—	Unverified
6	Zhang et al. (2016): Total lines of each Section, Operation Code Count, API Usage, Special Symbols Count, Asm File Pixel Intensity Feature, Bytes File Block Size Distribution, Bytes File N-Gram + Ensemble Learning (XGBoost)	Accuracy (10-fold)	1	—	Unverified
7	Orthrus	Accuracy (10-fold)	0.99	—	Unverified
8	Opcode-based Shallow CNN	Accuracy (10-fold)	0.99	—	Unverified
9	Hierarchical Convolutional Network	Accuracy (10-fold)	0.99	—	Unverified
10	SEA	Accuracy (10-fold)	0.99	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	GA Designed Deep CNN	Accuracy	0.99	—	Unverified
2	Gray-scale IMG CNN	Accuracy (10-fold)	0.98	—	Unverified
3	GRU + SVM	Accuracy	0.85	—	Unverified
4	FFNN + SVM	Accuracy	0.8	—	Unverified
5	CNN + SVM	Accuracy	0.77	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Levit-MC	Accuracy	96.6	—	Unverified