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 126–146 of 146 papers

TitleStatusHype
Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection—0
Using Convolutional Neural Networks for Classification of Malware represented as ImagesCode0
Deep learning at the shallow end: Malware classification for non-domain expertsCode0
TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time—0
Robust Neural Malware Detection Models for Emulation Sequence LearningCode0
Defending Malware Classification Networks Against Adversarial Perturbations with Non-Negative Weight Restrictions—0
Classification of Malware by Using Structural Entropy on Convolutional Neural NetworksCode0
Generative Models for Spear Phishing Posts on Social Media—0
Learning a Neural-network-based Representation for Open Set RecognitionCode0
Towards Building an Intelligent Anti-Malware System: A Deep Learning Approach using Support Vector Machine (SVM) for Malware ClassificationCode0
Computer activity learning from system call time series—0
Convolutional Neural Network for Classification of Malware Assembly CodeCode0
On the (Statistical) Detection of Adversarial Examples—0
SoK: Applying Machine Learning in Security - A Survey—0
A multi-task learning model for malware classification with useful file access pattern from API call sequence—0
One-Class SVM with Privileged Information and its Application to Malware Detection—0
Random Forest for Malware Classification—0
N-opcode Analysis for Android Malware Classification and Categorization—0
Adversarial Perturbations Against Deep Neural Networks for Malware Classification—0
Detection under Privileged Information—0
Novel Feature Extraction, Selection and Fusion for Effective Malware Family ClassificationCode0
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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