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	Hype
Data Augmentation for Opcode Sequence Based Malware Detection	Jun 22, 2021	Android Malware DetectionData Augmentation	—Unverified	0
CNN vs ELM for Image-Based Malware Classification	Mar 24, 2021	BIG-bench Machine LearningClassification	—Unverified	0
An Empirical Analysis of Image-Based Learning Techniques for Malware Classification	Mar 24, 2021	Deep LearningGeneral Classification	CodeCode Available	1
Deep Learning for Android Malware Defenses: a Systematic Literature Review	Mar 9, 2021	Android Malware DetectionDeep Learning	CodeCode Available	1
A Comparison of Word2Vec, HMM2Vec, and PCA2Vec for Malware Classification	Mar 7, 2021	ClassificationFeature Engineering	—Unverified	0
Malware Classification with GMM-HMM Models	Mar 3, 2021	ClassificationGeneral Classification	—Unverified	0
Malware Classification Using Long Short-Term Memory Models	Mar 3, 2021	ClassificationGeneral Classification	—Unverified	0
Malware Classification with Word Embedding Features	Mar 3, 2021	BIG-bench Machine LearningClassification	—Unverified	0
Adversarial Robustness with Non-uniform Perturbations	Feb 24, 2021	Adversarial RobustnessMalware Classification	CodeCode Available	0
Realizable Universal Adversarial Perturbations for Malware	Feb 12, 2021	Malware Classification	—Unverified	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

#	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