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
Malware Traffic Classification: Evaluation of Algorithms and an Automated Ground-truth Generation Pipeline	Oct 22, 2020	ClusteringGeneral Classification	—Unverified
Model-agnostic clean-label backdoor mitigation in cybersecurity environments	Jul 11, 2024	backdoor defenseClassification	—Unverified
Multimodal Techniques for Malware Classification	Jan 19, 2025	ClassificationMalware Classification	—Unverified
Benchmark Static API Call Datasets for Malware Family Classification	Nov 30, 2021	ClassificationMalware Classification	—Unverified
N-opcode Analysis for Android Malware Classification and Categorization	Jul 27, 2016	ClassificationGeneral Classification	—Unverified
One-Class SVM with Privileged Information and its Application to Malware Detection	Sep 26, 2016	Anomaly DetectionClassification	—Unverified
On the (Statistical) Detection of Adversarial Examples	Feb 21, 2017	Intrusion DetectionMalware Classification	—Unverified
OpCode-Based Malware Classification Using Machine Learning and Deep Learning Techniques	Apr 18, 2025	Android Malware DetectionDeep Learning	—Unverified
Projecting "better than randomly": How to reduce the dimensionality of very large datasets in a way that outperforms random projections	Jan 3, 2019	Dimensionality ReductionGeneral Classification	—Unverified
Trade-offs between membership privacy & adversarially robust learning	Jun 8, 2020	BIG-bench Machine LearningFairness	—Unverified

Show:10 25 50

← PrevPage 8 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