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 51–75 of 146 papers

TitleStatusHype
Holographic Global Convolutional Networks for Long-Range Prediction Tasks in Malware Detection—0
Image-Based Malware Classification Using QR and Aztec Codes—0
Impact of Feature Encoding on Malware Classification Explainability—0
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
Multimodal Techniques for Malware Classification—0
Benchmark Static API Call Datasets for Malware Family Classification—0
N-opcode Analysis for Android Malware Classification and Categorization—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