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
Malware Classification Leveraging NLP & Machine Learning for Enhanced Accuracy	Jun 19, 2025	Classificationfeature selection	CodeCode Available	0
Semantic Preprocessing for LLM-based Malware Analysis	Jun 13, 2025	Language ModelingLanguage Modelling	—Unverified	0
EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers	Jun 5, 2025	Malware AnalysisMalware Classification	CodeCode Available	2
Dynamic Malware Classification of Windows PE Files using CNNs and Greyscale Images Derived from Runtime API Call Argument Conversion	May 30, 2025	Malware ClassificationMalware Detection	—Unverified	0
Structure-based Anomaly Detection and Clustering	May 19, 2025	Anomaly DetectionClustering	—Unverified	0
Malware families discovery via Open-Set Recognition on Android manifest permissions	May 19, 2025	Malware ClassificationMalware Detection	—Unverified	0
Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings	Apr 25, 2025	AttributeMalware Classification	—Unverified	0
Scalable APT Malware Classification via Parallel Feature Extraction and GPU-Accelerated Learning	Apr 22, 2025	GPUMalware Classification	—Unverified	0
OpCode-Based Malware Classification Using Machine Learning and Deep Learning Techniques	Apr 18, 2025	Android Malware DetectionDeep Learning	—Unverified	0
Imbalanced malware classification: an approach based on dynamic classifier selection	Mar 30, 2025	Malware ClassificationMalware Detection	CodeCode Available	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