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 131–140 of 146 papers

Title	Date	Tasks	Status
Defending Malware Classification Networks Against Adversarial Perturbations with Non-Negative Weight Restrictions	Jun 23, 2018	ClassificationGeneral Classification	—Unverified
Designing Deep Convolutional Neural Networks using a Genetic Algorithm for Image-based Malware Classification	Jul 18, 2022	Evolutionary AlgorithmsMalware Classification	—Unverified
Detection of Advanced Malware by Machine Learning Techniques	Mar 7, 2019	BIG-bench Machine Learningfeature selection	—Unverified
Detection under Privileged Information	Mar 31, 2016	Face RecognitionMalware Classification	—Unverified
DRo: A data-scarce mechanism to revolutionize the performance of Deep Learning based Security Systems	Sep 12, 2021	Deep ClusteringMalware Classification	—Unverified
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
Effectiveness of Adversarial Examples and Defenses for Malware Classification	Sep 10, 2019	ClassificationGeneral Classification	—Unverified
Enhancing Efficiency and Privacy in Memory-Based Malware Classification through Feature Selection	Sep 30, 2023	Classificationfeature selection	—Unverified
Evaluating the Efficacy of Prompt-Engineered Large Multimodal Models Versus Fine-Tuned Vision Transformers in Image-Based Security Applications	Mar 26, 2024	Malware ClassificationPrompt Engineering	—Unverified
Examining Adversarial Learning against Graph-based IoT Malware Detection Systems	Feb 12, 2019	Adversarial AttackGeneral Classification	—Unverified

Show:10 25 50

← PrevPage 14 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