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
Generation & Evaluation of Adversarial Examples for Malware Obfuscation—0
SoK: Applying Machine Learning in Security - A Survey—0
Steganographic Capacity of Deep Learning Models—0
Structure-based Anomaly Detection and Clustering—0
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time—0
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time (Extended Version)—0
To believe or not to believe: Validating explanation fidelity for dynamic malware analysis—0
Poison Forensics: Traceback of Data Poisoning Attacks in Neural Networks—0
Realizable Universal Adversarial Perturbations for Malware—0
XAI and Android Malware Models—0
Enhancing Efficiency and Privacy in Memory-Based Malware Classification through Feature Selection—0
Evaluating the Efficacy of Prompt-Engineered Large Multimodal Models Versus Fine-Tuned Vision Transformers in Image-Based Security Applications—0
Examining Adversarial Learning against Graph-based IoT Malware Detection Systems—0
Explainable Artificial Intelligence (XAI) for Malware Analysis: A Survey of Techniques, Applications, and Open Challenges—0
Exploring Adversarial Examples in Malware Detection—0
Feature-level Malware Obfuscation in Deep Learning—0
Generative Adversarial Networks and Image-Based Malware Classification—0
Generative Models for Spear Phishing Posts on Social Media—0
Graph Neural Network-based Android Malware Classification with Jumping Knowledge—0
Combating Concept Drift with Explanatory Detection and Adaptation for Android Malware Classification—0
HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples—0
Exploring Optimal Deep Learning Models for Image-based Malware Variant Classification—0
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
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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