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 121–130 of 146 papers

Title	Date	Tasks	Status
Deep Transfer Learning for Static Malware Classification	Dec 18, 2018	Binary ClassificationClassification	CodeCode Available
Orthrus: A Bimodal Learning Architecture for Malware Classification	Sep 28, 2020	ClassificationDeep Learning	CodeCode Available
Less is More: A privacy-respecting Android malware classifier using Federated Learning	Jul 16, 2020	Federated LearningMalware Classification	CodeCode Available
Using Convolutional Neural Networks for Classification of Malware represented as Images	Aug 27, 2018	General ClassificationMalware Classification	CodeCode Available
DAEMON: Dataset-Agnostic Explainable Malware Classification Using Multi-Stage Feature Mining	Aug 4, 2020	ClassificationGeneral Classification	CodeCode Available
Robust Neural Malware Detection Models for Emulation Sequence Learning	Jun 28, 2018	Computer SecurityMalware Classification	CodeCode Available
Deep-Net: Deep Neural Network for Cyber Security Use Cases	Dec 9, 2018	Fraud DetectionGeneral Classification	CodeCode Available
Transfer Learning for Image-Based Malware Classification	Jan 21, 2019	ClassificationGeneral Classification	CodeCode Available
Classification of Malware by Using Structural Entropy on Convolutional Neural Networks	Apr 27, 2018	General ClassificationMalware Classification	CodeCode Available
Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy	Jun 19, 2025	Classificationfeature selection	CodeCode Available

Show:10 25 50

← PrevPage 13 of 15Next →

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