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 51–75 of 146 papers

TitleStatusHype
Self-Supervised Vision Transformers for Malware DetectionCode1
On the Limitations of Continual Learning for Malware ClassificationCode1
On deceiving malware classification with section injectionCode1
Designing Deep Convolutional Neural Networks using a Genetic Algorithm for Image-based Malware Classification—0
AI-based Malware and Ransomware Detection Models—0
Generative Adversarial Networks and Image-Based Malware Classification—0
Representation learning with function call graph transformations for malware open set recognition—0
Backdooring Explainable Machine Learning—0
Malceiver: Perceiver with Hierarchical and Multi-modal Features for Android Malware Detection—0
Bayesian Deep Learning for Graphs—0
Graph Neural Network-based Android Malware Classification with Jumping Knowledge—0
Comprehensive Efficiency Analysis of Machine Learning Algorithms for Developing Hardware-Based Cybersecurity Countermeasures—0
An Ensemble of Pre-trained Transformer Models For Imbalanced Multiclass Malware ClassificationCode1
Malware Classification Using Static Disassembly and Machine LearningCode1
Benchmark Static API Call Datasets for Malware Family Classification—0
A Comprehensive Study on Learning-Based PE Malware Family Classification MethodsCode1
Poison Forensics: Traceback of Data Poisoning Attacks in Neural Networks—0
DRo: A data-scarce mechanism to revolutionize the performance of Deep Learning based Security Systems—0
Malware Classification Using Transfer Learning—0
Malware Classification Using Deep Boosted Learning—0
Data Augmentation for Opcode Sequence Based Malware Detection—0
CNN vs ELM for Image-Based Malware Classification—0
An Empirical Analysis of Image-Based Learning Techniques for Malware ClassificationCode1
Deep Learning for Android Malware Defenses: a Systematic Literature ReviewCode1
A Comparison of Word2Vec, HMM2Vec, and PCA2Vec for 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