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

Recently AddedMost HypedMost ActiveNeeds VerificationMost Verified

Showing 1–50 of 146 papers

TitleStatusHype
EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware ClassifiersCode2
Self-Supervised Vision Transformers for Malware DetectionCode1
On deceiving malware classification with section injectionCode1
Malware Classification Using Static Disassembly and Machine LearningCode1
An Empirical Analysis of Image-Based Learning Techniques for Malware ClassificationCode1
On the Limitations of Continual Learning for Malware ClassificationCode1
Assemblage: Automatic Binary Dataset Construction for Machine LearningCode1
A Comprehensive Study on Learning-Based PE Malware Family Classification MethodsCode1
Why an Android App is Classified as Malware? Towards Malware Classification InterpretationCode1
A New Burrows Wheeler Transform Markov DistanceCode1
HYDRA: A multimodal deep learning framework for malware classificationCode1
A Framework for Enhancing Deep Neural Networks Against Adversarial MalwareCode1
Explanation-Guided Backdoor Poisoning Attacks Against Malware ClassifiersCode1
An Ensemble of Pre-trained Transformer Models For Imbalanced Multiclass Malware ClassificationCode1
MalCL: Leveraging GAN-Based Generative Replay to Combat Catastrophic Forgetting in Malware ClassificationCode1
Nebula: Self-Attention for Dynamic Malware AnalysisCode1
Recasting Self-Attention with Holographic Reduced RepresentationsCode1
Deep Learning for Android Malware Defenses: a Systematic Literature ReviewCode1
Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model PerformanceCode1
A Dynamic Weighted Federated Learning for Android Malware ClassificationCode1
Case Study-Based Approach of Quantum Machine Learning in Cybersecurity: Quantum Support Vector Machine for Malware Classification and Protection—0
Can Feature Engineering Help Quantum Machine Learning for Malware Detection?—0
A Natural Language Processing Approach to Malware Classification—0
Behavioral Malware Classification using Convolutional Recurrent Neural Networks—0
Bayesian Deep Learning for Graphs—0
A multi-task learning model for malware classification with useful file access pattern from API call sequence—0
A Comparison of Graph Neural Networks for Malware Classification—0
Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks—0
A Malware Classification Survey on Adversarial Attacks and Defences—0
Dynamic Malware Classification of Windows PE Files using CNNs and Greyscale Images Derived from Runtime API Call Argument Conversion—0
Effectiveness of Adversarial Examples and Defenses for Malware Classification—0
Enhancing Efficiency and Privacy in Memory-Based Malware Classification through Feature Selection—0
Deep Learning-Driven Malware Classification with API Call Sequence Analysis and Concept Drift Handling—0
A Survey of Machine Learning Methods and Challenges for Windows Malware Classification—0
Detection of Advanced Malware by Machine Learning Techniques—0
A short review on Applications of Deep learning for Cyber security—0
A Hierarchical Convolutional Neural Network for Malware Classification—0
Defending Malware Classification Networks Against Adversarial Perturbations with Non-Negative Weight Restrictions—0
Designing Deep Convolutional Neural Networks using a Genetic Algorithm for Image-based Malware Classification—0
Detection under Privileged Information—0
Data Augmentation for Opcode Sequence Based Malware Detection—0
Computer activity learning from system call time series—0
Comprehensive Efficiency Analysis of Machine Learning Algorithms for Developing Hardware-Based Cybersecurity Countermeasures—0
Deep Learning and Open Set Malware Classification: A Survey—0
A Novel Feature Representation for Malware Classification—0
Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection—0
AuthAttLyzer-V2: Unveiling Code Authorship Attribution using Enhanced Ensemble Learning Models & Generating Benchmark Dataset—0
Deep-Net: Deep Neural Network for Cyber Security Use Cases—0
Backdooring Explainable Machine Learning—0
CNN vs ELM for Image-Based Malware Classification—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