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
Cluster Analysis and Concept Drift Detection in Malware	Feb 19, 2025	ClusteringDrift Detection	—Unverified
CNN-LSTM and Transfer Learning Models for Malware Classification based on Opcodes and API Calls	May 4, 2024	ClassificationMalware Classification	—Unverified
CNN vs ELM for Image-Based Malware Classification	Mar 24, 2021	BIG-bench Machine LearningClassification	—Unverified
Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection	Sep 16, 2018	BIG-bench Machine LearningDeep Learning	—Unverified
Comprehensive Efficiency Analysis of Machine Learning Algorithms for Developing Hardware-Based Cybersecurity Countermeasures	Jan 5, 2022	BIG-bench Machine LearningEnsemble Learning	—Unverified
Computer activity learning from system call time series	Nov 6, 2017	General ClassificationMalware Classification	—Unverified
Data Augmentation for Opcode Sequence Based Malware Detection	Jun 22, 2021	Android Malware DetectionData Augmentation	—Unverified
Deep Learning and Open Set Malware Classification: A Survey	Apr 8, 2020	BIG-bench Machine LearningClassification	—Unverified
Deep Learning-Driven Malware Classification with API Call Sequence Analysis and Concept Drift Handling	Feb 12, 2025	ClassificationDeep Learning	—Unverified
Deep-Net: Deep Neural Network for Cyber Security Use Cases	Oct 12, 2018	Fraud DetectionMalware 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