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 1–50 of 146 papers

TitleStatusHype
EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware ClassifiersCode2
A New Burrows Wheeler Transform Markov DistanceCode1
Deep Learning for Android Malware Defenses: a Systematic Literature ReviewCode1
An Ensemble of Pre-trained Transformer Models For Imbalanced Multiclass Malware ClassificationCode1
An Empirical Analysis of Image-Based Learning Techniques for Malware ClassificationCode1
Malware Classification Using Static Disassembly and Machine LearningCode1
A Dynamic Weighted Federated Learning for Android Malware ClassificationCode1
MalCL: Leveraging GAN-Based Generative Replay to Combat Catastrophic Forgetting in Malware ClassificationCode1
HYDRA: A multimodal deep learning framework for malware classificationCode1
A Framework for Enhancing Deep Neural Networks Against Adversarial MalwareCode1
Why an Android App is Classified as Malware? Towards Malware Classification InterpretationCode1
Self-Supervised Vision Transformers for Malware DetectionCode1
On deceiving malware classification with section injectionCode1
Recasting Self-Attention with Holographic Reduced RepresentationsCode1
On the Limitations of Continual Learning for Malware ClassificationCode1
Explanation-Guided Backdoor Poisoning Attacks Against Malware ClassifiersCode1
A Comprehensive Study on Learning-Based PE Malware Family Classification MethodsCode1
Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model PerformanceCode1
Assemblage: Automatic Binary Dataset Construction for Machine LearningCode1
Nebula: Self-Attention for Dynamic Malware AnalysisCode1
Classification of Malware by Using Structural Entropy on Convolutional Neural NetworksCode0
Using Convolutional Neural Networks for Classification of Malware represented as ImagesCode0
Deep Transfer Learning for Static Malware ClassificationCode0
Robust Neural Malware Detection Models for Emulation Sequence LearningCode0
Sequential Embedding-based Attentive (SEA) classifier for malware classificationCode0
Orthrus: A Bimodal Learning Architecture for Malware ClassificationCode0
Activation Analysis of a Byte-Based Deep Neural Network for Malware ClassificationCode0
Transfer Learning for Image-Based Malware ClassificationCode0
A Convolutional Transformation Network for Malware ClassificationCode0
Deep-Net: Deep Neural Network for Cyber Security Use CasesCode0
Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine (SVM) for Malware ClassificationCode0
Deep learning at the shallow end: Malware classification for non-domain expertsCode0
Malware Classification using Deep Learning based Feature Extraction and Wrapper based Feature Selection TechniqueCode0
Applications of Graph Integration to Function Comparison and Malware ClassificationCode0
Convolutional Neural Network for Classification of Malware Assembly CodeCode0
Malware Classification Leveraging NLP & Machine Learning for Enhanced AccuracyCode0
Novel Feature Extraction, Selection and Fusion for Effective Malware Family ClassificationCode0
Integration of Static and Dynamic Analysis for Malware Family Classification with Composite Neural NetworkCode0
Adversarial Robustness with Non-uniform PerturbationsCode0
DAEMON: Dataset-Agnostic Explainable Malware Classification Using Multi-Stage Feature MiningCode0
KiloGrams: Very Large N-Grams for Malware ClassificationCode0
Imbalanced malware classification: an approach based on dynamic classifier selectionCode0
Learning a Neural-network-based Representation for Open Set RecognitionCode0
Dynamic data fusion using multi-input models for malware classificationCode0
Accelerating Malware Classification: A Vision Transformer SolutionCode0
High-resolution Image-based Malware Classification using Multiple Instance LearningCode0
Less is More: A privacy-respecting Android malware classifier using Federated LearningCode0
Case Study-Based Approach of Quantum Machine Learning in Cybersecurity: Quantum Support Vector Machine for Malware Classification and Protection—0
Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks—0
Can Feature Engineering Help Quantum Machine Learning for Malware Detection?—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