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 31–40 of 146 papers

Title	Date	Tasks	Status
A Malware Classification Survey on Adversarial Attacks and Defences	Dec 15, 2023	Adversarial AttackClassification	—Unverified
A Comparison of Graph Neural Networks for Malware Classification	Mar 22, 2023	ClassificationGraph Classification	—Unverified
Deep-Net: Deep Neural Network for Cyber Security Use Cases	Oct 12, 2018	Fraud DetectionMalware Classification	—Unverified
A Survey of Machine Learning Methods and Challenges for Windows Malware Classification	Jun 15, 2020	BIG-bench Machine LearningGeneral Classification	—Unverified
A short review on Applications of Deep learning for Cyber security	Dec 15, 2018	Android Malware DetectionBIG-bench Machine Learning	—Unverified
A Hierarchical Convolutional Neural Network for Malware Classification	Sep 30, 2019	ClassificationGeneral Classification	—Unverified
Data Augmentation for Opcode Sequence Based Malware Detection	Jun 22, 2021	Android Malware DetectionData Augmentation	—Unverified
Computer activity learning from system call time series	Nov 6, 2017	General ClassificationMalware Classification	—Unverified
Comprehensive Efficiency Analysis of Machine Learning Algorithms for Developing Hardware-Based Cybersecurity Countermeasures	Jan 5, 2022	BIG-bench Machine LearningEnsemble Learning	—Unverified
A Novel Feature Representation for Malware Classification	Oct 18, 2022	ClassificationDescriptive	—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