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 21–30 of 146 papers

Title	Date	Tasks	Status	Hype
Revisiting Static Feature-Based Android Malware Detection	Sep 11, 2024	Android Malware DetectionComputer Security	—Unverified	0
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	0
Model-agnostic clean-label backdoor mitigation in cybersecurity environments	Jul 11, 2024	backdoor defenseClassification	—Unverified	0
AuthAttLyzer-V2: Unveiling Code Authorship Attribution using Enhanced Ensemble Learning Models & Generating Benchmark Dataset	Jun 28, 2024	Authorship AttributionEnsemble Learning	—Unverified	0
kNN Classification of Malware Data Dependency Graph Features	Jun 4, 2024	ClassificationMalware Classification	—Unverified	0
Combating Concept Drift with Explanatory Detection and Adaptation for Android Malware Classification	May 7, 2024	Drift DetectionMalware Classification	—Unverified	0
Assemblage: Automatic Binary Dataset Construction for Machine Learning	May 7, 2024	Malware Classification	CodeCode Available	1
CNN-LSTM and Transfer Learning Models for Malware Classification based on Opcodes and API Calls	May 4, 2024	ClassificationMalware Classification	—Unverified	0
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	0
Holographic Global Convolutional Networks for Long-Range Prediction Tasks in Malware Detection	Mar 23, 2024	Malware ClassificationMalware Detection	—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