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 141–146 of 146 papers

Title	Date	Tasks	Status
Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges	Sep 9, 2024	Explainable artificial intelligenceExplainable Artificial Intelligence (XAI)	—Unverified
Exploring Adversarial Examples in Malware Detection	Oct 18, 2018	Feature EngineeringGeneral Classification	—Unverified
Feature-level Malware Obfuscation in Deep Learning	Feb 10, 2020	Data AugmentationDeep Learning	—Unverified
Generative Adversarial Networks and Image-Based Malware Classification	Jun 8, 2022	BIG-bench Machine LearningClassification	—Unverified
Generative Models for Spear Phishing Posts on Social Media	Feb 14, 2018	ClusteringComputer Security	—Unverified
Graph Neural Network-based Android Malware Classification with Jumping Knowledge	Jan 19, 2022	Android Malware DetectionGraph Neural Network	—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