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 101–146 of 146 papers

TitleStatusHype
Intelligent Systems Design for Malware Classification Under Adversarial Conditions—0
Interpreting GNN-based IDS Detections Using Provenance Graph Structural Features—0
kNN Classification of Malware Data Dependency Graph Features—0
Lempel-Ziv Networks—0
Malceiver: Perceiver with Hierarchical and Multi-modal Features for Android Malware Detection—0
MalMixer: Few-Shot Malware Classification with Retrieval-Augmented Semi-Supervised Learning—0
AI-based Malware and Ransomware Detection Models—0
Malware Classification from Memory Dumps Using Machine Learning, Transformers, and Large Language Models—0
Malware Classification using a Hybrid Hidden Markov Model-Convolutional Neural Network—0
Malware Classification Using Deep Boosted Learning—0
Malware Classification using Deep Neural Networks: Performance Evaluation and Applications in Edge Devices—0
Malware Classification Using Long Short-Term Memory Models—0
Malware Classification Using Transfer Learning—0
Malware Classification with GMM-HMM Models—0
Malware Classification with Word Embedding Features—0
Malware Detection using Machine Learning and Deep Learning—0
Malware families discovery via Open-Set Recognition on Android manifest permissions—0
Malware Traffic Classification: Evaluation of Algorithms and an Automated Ground-truth Generation Pipeline—0
Model-agnostic clean-label backdoor mitigation in cybersecurity environments—0
Dynamic data fusion using multi-input models for malware classificationCode0
Deep Transfer Learning for Static Malware ClassificationCode0
Orthrus: A Bimodal Learning Architecture for Malware ClassificationCode0
Less is More: A privacy-respecting Android malware classifier using Federated LearningCode0
Using Convolutional Neural Networks for Classification of Malware represented as ImagesCode0
DAEMON: Dataset-Agnostic Explainable Malware Classification Using Multi-Stage Feature MiningCode0
Robust Neural Malware Detection Models for Emulation Sequence LearningCode0
Deep-Net: Deep Neural Network for Cyber Security Use CasesCode0
Transfer Learning for Image-Based Malware ClassificationCode0
Classification of Malware by Using Structural Entropy on Convolutional Neural NetworksCode0
Malware Classification Leveraging NLP & Machine Learning for Enhanced AccuracyCode0
A Convolutional Transformation Network for Malware ClassificationCode0
Activation Analysis of a Byte-Based Deep Neural Network for Malware ClassificationCode0
Malware Classification using Deep Learning based Feature Extraction and Wrapper based Feature Selection TechniqueCode0
Novel Feature Extraction, Selection and Fusion for Effective Malware Family ClassificationCode0
High-resolution Image-based Malware Classification using Multiple Instance LearningCode0
Applications of Graph Integration to Function Comparison and Malware ClassificationCode0
Deep learning at the shallow end: Malware classification for non-domain expertsCode0
Convolutional Neural Network for Classification of Malware Assembly CodeCode0
Imbalanced malware classification: an approach based on dynamic classifier selectionCode0
Accelerating Malware Classification: A Vision Transformer SolutionCode0
Integration of Static and Dynamic Analysis for Malware Family Classification with Composite Neural NetworkCode0
Adversarial Robustness with Non-uniform PerturbationsCode0
Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine (SVM) for Malware ClassificationCode0
KiloGrams: Very Large N-Grams for Malware ClassificationCode0
Sequential Embedding-based Attentive (SEA) classifier for malware classificationCode0
Learning a Neural-network-based Representation for Open Set RecognitionCode0
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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