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	Hype
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version)	Feb 2, 2024	Decision MakingMalware Classification	—Unverified	0
A Malware Classification Survey on Adversarial Attacks and Defences	Dec 15, 2023	Adversarial AttackClassification	—Unverified	0
High-resolution Image-based Malware Classification using Multiple Instance Learning	Nov 21, 2023	ClassificationMalware Classification	CodeCode Available	0
Enhancing Efficiency and Privacy in Memory-Based Malware Classification through Feature Selection	Sep 30, 2023	Classificationfeature selection	—Unverified	0
Nebula: Self-Attention for Dynamic Malware Analysis	Sep 19, 2023	Malware AnalysisMalware Classification	CodeCode Available	1
Malware Classification using Deep Neural Networks: Performance Evaluation and Applications in Edge Devices	Aug 21, 2023	Edge-computingMalware Classification	—Unverified	0
Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model Performance	Jul 27, 2023	ClassificationMalware Classification	CodeCode Available	1
Impact of Feature Encoding on Malware Classification Explainability	Jul 10, 2023	ClassificationExplainable artificial intelligence	—Unverified	0
A Natural Language Processing Approach to Malware Classification	Jul 7, 2023	ClassificationFeature Engineering	—Unverified	0
Steganographic Capacity of Deep Learning Models	Jun 25, 2023	Deep LearningMalware 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