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 71–80 of 146 papers

Title	Date	Tasks	Status
RNNSecureNet: Recurrent neural networks for Cyber security use-cases	Jan 5, 2019	Fraud DetectionGeneral Classification	—Unverified
RNNSecureNet: Recurrent neural networks for Cybersecurity use-cases	Oct 12, 2018	Fraud DetectionMalware Classification	—Unverified
Scalable APT Malware Classification via Parallel Feature Extraction and GPU-Accelerated Learning	Apr 22, 2025	GPUMalware Classification	—Unverified
Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings	Apr 25, 2025	AttributeMalware Classification	—Unverified
Semantic Preprocessing for LLM-based Malware Analysis	Jun 13, 2025	Language ModelingLanguage Modelling	—Unverified
Generation & Evaluation of Adversarial Examples for Malware Obfuscation	Apr 9, 2019	General ClassificationMalware Classification	—Unverified
SoK: Applying Machine Learning in Security - A Survey	Nov 10, 2016	BIG-bench Machine LearningIntrusion Detection	—Unverified
Steganographic Capacity of Deep Learning Models	Jun 25, 2023	Deep LearningMalware Classification	—Unverified
Structure-based Anomaly Detection and Clustering	May 19, 2025	Anomaly DetectionClustering	—Unverified
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time	Jul 20, 2018	Active LearningGeneral Classification	—Unverified

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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