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
Malware Classification Leveraging NLP & Machine Learning for Enhanced AccuracyCode0
Semantic Preprocessing for LLM-based Malware Analysis—0
EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware ClassifiersCode2
Dynamic Malware Classification of Windows PE Files using CNNs and Greyscale Images Derived from Runtime API Call Argument Conversion—0
Malware families discovery via Open-Set Recognition on Android manifest permissions—0
Structure-based Anomaly Detection and Clustering—0
Semantic-Aware Contrastive Fine-Tuning: Boosting Multimodal Malware Classification with Discriminative Embeddings—0
Scalable APT Malware Classification via Parallel Feature Extraction and GPU-Accelerated Learning—0
OpCode-Based Malware Classification Using Machine Learning and Deep Learning Techniques—0
Imbalanced malware classification: an approach based on dynamic classifier selectionCode0
Malware Classification from Memory Dumps Using Machine Learning, Transformers, and Large Language Models—0
Cluster Analysis and Concept Drift Detection in Malware—0
Deep Learning-Driven Malware Classification with API Call Sequence Analysis and Concept Drift Handling—0
Multimodal Techniques for Malware Classification—0
MalCL: Leveraging GAN-Based Generative Replay to Combat Catastrophic Forgetting in Malware ClassificationCode1
Malware Classification using a Hybrid Hidden Markov Model-Convolutional Neural Network—0
Image-Based Malware Classification Using QR and Aztec Codes—0
XAI and Android Malware Models—0
Accelerating Malware Classification: A Vision Transformer SolutionCode0
MalMixer: Few-Shot Malware Classification with Retrieval-Augmented Semi-Supervised Learning—0
Revisiting Static Feature-Based Android Malware Detection—0
Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges—0
Model-agnostic clean-label backdoor mitigation in cybersecurity environments—0
AuthAttLyzer-V2: Unveiling Code Authorship Attribution using Enhanced Ensemble Learning Models & Generating Benchmark Dataset—0
kNN Classification of Malware Data Dependency Graph Features—0
Combating Concept Drift with Explanatory Detection and Adaptation for Android Malware Classification—0
Assemblage: Automatic Binary Dataset Construction for Machine LearningCode1
CNN-LSTM and Transfer Learning Models for Malware Classification based on Opcodes and API Calls—0
Evaluating the Efficacy of Prompt-Engineered Large Multimodal Models Versus Fine-Tuned Vision Transformers in Image-Based Security Applications—0
Holographic Global Convolutional Networks for Long-Range Prediction Tasks in Malware Detection—0
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version)—0
A Malware Classification Survey on Adversarial Attacks and Defences—0
High-resolution Image-based Malware Classification using Multiple Instance LearningCode0
Enhancing Efficiency and Privacy in Memory-Based Malware Classification through Feature Selection—0
Nebula: Self-Attention for Dynamic Malware AnalysisCode1
Malware Classification using Deep Neural Networks: Performance Evaluation and Applications in Edge Devices—0
Decoding the Secrets of Machine Learning in Malware Classification: A Deep Dive into Datasets, Feature Extraction, and Model PerformanceCode1
Impact of Feature Encoding on Malware Classification Explainability—0
A Natural Language Processing Approach to Malware Classification—0
Steganographic Capacity of Deep Learning Models—0
Case Study-Based Approach of Quantum Machine Learning in Cybersecurity: Quantum Support Vector Machine for Malware Classification and Protection—0
Interpreting GNN-based IDS Detections Using Provenance Graph Structural Features—0
Recasting Self-Attention with Holographic Reduced RepresentationsCode1
Quantum Machine Learning for Malware Classification—0
Can Feature Engineering Help Quantum Machine Learning for Malware Detection?—0
A Comparison of Graph Neural Networks for Malware Classification—0
Sequential Embedding-based Attentive (SEA) classifier for malware classificationCode0
Lempel-Ziv Networks—0
A Dynamic Weighted Federated Learning for Android Malware ClassificationCode1
A Novel Feature Representation 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