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 76–100 of 146 papers

TitleStatusHype
Malware Classification with GMM-HMM Models—0
Malware Classification Using Long Short-Term Memory Models—0
Malware Classification with Word Embedding Features—0
Adversarial Robustness with Non-uniform PerturbationsCode0
Realizable Universal Adversarial Perturbations for Malware—0
Classifying Malware Using Function Representations in a Static Call Graph—0
Classifying Malware Images with Convolutional Neural Network Models—0
Malware Traffic Classification: Evaluation of Algorithms and an Automated Ground-truth Generation Pipeline—0
Orthrus: A Bimodal Learning Architecture for Malware ClassificationCode0
DAEMON: Dataset-Agnostic Explainable Malware Classification Using Multi-Stage Feature MiningCode0
Less is More: A privacy-respecting Android malware classifier using Federated LearningCode0
A Survey of Machine Learning Methods and Challenges for Windows Malware Classification—0
Trade-offs between membership privacy & adversarially robust learning—0
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
Exploring Optimal Deep Learning Models for Image-based Malware Variant Classification—0
Deep Learning and Open Set Malware Classification: A Survey—0
Explanation-Guided Backdoor Poisoning Attacks Against Malware ClassifiersCode1
Feature-level Malware Obfuscation in Deep Learning—0
Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks—0
A New Burrows Wheeler Transform Markov DistanceCode1
Integration of Static and Dynamic Analysis for Malware Family Classification with Composite Neural NetworkCode0
Malware Classification using Deep Learning based Feature Extraction and Wrapper based Feature Selection TechniqueCode0
A Hierarchical Convolutional Neural Network 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