SOTAVerified

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

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Showing 1–25 of 146 papers

TitleStatusHype
EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware ClassifiersCode2
MalCL: Leveraging GAN-Based Generative Replay to Combat Catastrophic Forgetting in Malware ClassificationCode1
Assemblage: Automatic Binary Dataset Construction for Machine LearningCode1
Nebula: Self-Attention for Dynamic Malware AnalysisCode1
Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model PerformanceCode1
Recasting Self-Attention with Holographic Reduced RepresentationsCode1
A Dynamic Weighted Federated Learning for Android Malware ClassificationCode1
Self-Supervised Vision Transformers for Malware DetectionCode1
On the Limitations of Continual Learning for Malware ClassificationCode1
On deceiving malware classification with section injectionCode1
An Ensemble of Pre-trained Transformer Models For Imbalanced Multiclass Malware ClassificationCode1
Malware Classification Using Static Disassembly and Machine LearningCode1
A Comprehensive Study on Learning-Based PE Malware Family Classification MethodsCode1
An Empirical Analysis of Image-Based Learning Techniques for Malware ClassificationCode1
Deep Learning for Android Malware Defenses: a Systematic Literature ReviewCode1
HYDRA: A multimodal deep learning framework for malware classificationCode1
Why an Android App is Classified as Malware? Towards Malware Classification InterpretationCode1
A Framework for Enhancing Deep Neural Networks Against Adversarial MalwareCode1
Explanation-Guided Backdoor Poisoning Attacks Against Malware ClassifiersCode1
A New Burrows Wheeler Transform Markov DistanceCode1
Malware Classification Leveraging NLP & Machine Learning for Enhanced AccuracyCode0
Semantic Preprocessing for LLM-based Malware Analysis—0
Dynamic Malware Classification of Windows PE Files using CNNs and Greyscale Images Derived from Runtime API Call Argument Conversion—0
Malware families discovery via Open-Set Recognition on Android manifest permissions—0
Structure-based Anomaly Detection and Clustering—0
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All datasetsMicrosoft Malware Classification ChallengeMalimg DatasetMaleVis

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MalConvAccuracy (10-fold)9,641—Unverified
2TPOT ClassifierAccuracy (5-fold)98.94—Unverified
3CNN BiLSTM - Reb SamplAccuracy (5-fold)98.2—Unverified
4Ahmadi 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
5HYDRAAccuracy (10-fold)1—Unverified
6Zhang 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
7OrthrusAccuracy (10-fold)0.99—Unverified
8Opcode-based Shallow CNNAccuracy (10-fold)0.99—Unverified
9Hierarchical Convolutional NetworkAccuracy (10-fold)0.99—Unverified
10SEAAccuracy (10-fold)0.99—Unverified
#ModelMetricClaimedVerifiedStatus
1GA Designed Deep CNNAccuracy0.99—Unverified
2Gray-scale IMG CNNAccuracy (10-fold)0.98—Unverified
3GRU + SVMAccuracy0.85—Unverified
4FFNN + SVMAccuracy0.8—Unverified
5CNN + SVMAccuracy0.77—Unverified
#ModelMetricClaimedVerifiedStatus
1Levit-MCAccuracy96.6—Unverified