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 26–50 of 146 papers

Title	Date	Tasks	Status	Score
Orthrus: A Bimodal Learning Architecture for Malware Classification	Sep 28, 2020	ClassificationDeep Learning	CodeCode Available	5
Activation Analysis of a Byte-Based Deep Neural Network for Malware Classification	Mar 12, 2019	BIG-bench Machine LearningFeature Engineering	CodeCode Available	5
Transfer Learning for Image-Based Malware Classification	Jan 21, 2019	ClassificationGeneral Classification	CodeCode Available	5
A Convolutional Transformation Network for Malware Classification	Sep 16, 2019	ClassificationGeneral Classification	CodeCode Available	5
Deep-Net: Deep Neural Network for Cyber Security Use Cases	Dec 9, 2018	Fraud DetectionGeneral Classification	CodeCode Available	5
Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine (SVM) for Malware Classification	Dec 31, 2017	General ClassificationMalware Classification	CodeCode Available	5
Deep learning at the shallow end: Malware classification for non-domain experts	Jul 22, 2018	ClassificationGeneral Classification	CodeCode Available	5
Malware Classification using Deep Learning based Feature Extraction and Wrapper based Feature Selection Technique	Oct 24, 2019	Feature Engineeringfeature selection	CodeCode Available	5
Applications of Graph Integration to Function Comparison and Malware Classification	Oct 11, 2018	ClassificationGeneral Classification	CodeCode Available	5
Convolutional Neural Network for Classification of Malware Assembly Code	Oct 27, 2017	ClassificationGeneral Classification	CodeCode Available	5
Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy	Jun 19, 2025	Classificationfeature selection	CodeCode Available	5
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification	Nov 13, 2015	Computer SecurityGeneral Classification	CodeCode Available	5
Integration of Static and Dynamic Analysis for Malware Family Classification with Composite Neural Network	Dec 24, 2019	ClassificationGeneral Classification	CodeCode Available	5
Adversarial Robustness with Non-uniform Perturbations	Feb 24, 2021	Adversarial RobustnessMalware Classification	CodeCode Available	5
DAEMON: Dataset-Agnostic Explainable Malware Classification Using Multi-Stage Feature Mining	Aug 4, 2020	ClassificationGeneral Classification	CodeCode Available	5
KiloGrams: Very Large N-Grams for Malware Classification	Aug 1, 2019	ClassificationGeneral Classification	CodeCode Available	5
Imbalanced malware classification: an approach based on dynamic classifier selection	Mar 30, 2025	Malware ClassificationMalware Detection	CodeCode Available	5
Learning a Neural-network-based Representation for Open Set Recognition	Feb 12, 2018	General ClassificationMalware Classification	CodeCode Available	5
Dynamic data fusion using multi-input models for malware classification	Sep 21, 2019	ClassificationGeneral Classification	CodeCode Available	5
Accelerating Malware Classification: A Vision Transformer Solution	Sep 28, 2024	Classificationimage-classification	CodeCode Available	5
High-resolution Image-based Malware Classification using Multiple Instance Learning	Nov 21, 2023	ClassificationMalware Classification	CodeCode Available	5
Less is More: A privacy-respecting Android malware classifier using Federated Learning	Jul 16, 2020	Federated LearningMalware Classification	CodeCode Available	5
Case Study-Based Approach of Quantum Machine Learning in Cybersecurity: Quantum Support Vector Machine for Malware Classification and Protection	Jun 1, 2023	Malware ClassificationQuantum Machine Learning	—Unverified	0
Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks	Feb 7, 2020	Cyber Attack DetectionData Poisoning	—Unverified	0
Can Feature Engineering Help Quantum Machine Learning for Malware Detection?	May 3, 2023	Feature Engineeringfeature selection	—Unverified	0

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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