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 101–125 of 146 papers

TitleStatusHype
Dynamic data fusion using multi-input models for malware classificationCode0
A Convolutional Transformation Network for Malware ClassificationCode0
Effectiveness of Adversarial Examples and Defenses for Malware Classification—0
KiloGrams: Very Large N-Grams for Malware ClassificationCode0
Intelligent Systems Design for Malware Classification Under Adversarial Conditions—0
To believe or not to believe: Validating explanation fidelity for dynamic malware analysis—0
Generation & Evaluation of Adversarial Examples for Malware Obfuscation—0
Malware Detection using Machine Learning and Deep Learning—0
Understanding the efficacy, reliability and resiliency of computer vision techniques for malware detection and future research directions—0
Activation Analysis of a Byte-Based Deep Neural Network for Malware ClassificationCode0
Detection of Advanced Malware by Machine Learning Techniques—0
Examining Adversarial Learning against Graph-based IoT Malware Detection Systems—0
Transfer Learning for Image-Based Malware ClassificationCode0
RNNSecureNet: Recurrent neural networks for Cyber security use-cases—0
Projecting "better than randomly": How to reduce the dimensionality of very large datasets in a way that outperforms random projections—0
Deep Transfer Learning for Static Malware ClassificationCode0
A short review on Applications of Deep learning for Cyber security—0
Deep-Net: Deep Neural Network for Cyber Security Use CasesCode0
Behavioral Malware Classification using Convolutional Recurrent Neural Networks—0
Exploring Adversarial Examples in Malware Detection—0
Deep-Net: Deep Neural Network for Cyber Security Use Cases—0
RNNSecureNet: Recurrent neural networks for Cybersecurity use-cases—0
Applications of Graph Integration to Function Comparison and Malware ClassificationCode0
An End-to-End Deep Learning Architecture for Classification of Malware’s Binary Content—0
HashTran-DNN: A Framework for Enhancing Robustness of Deep Neural Networks against Adversarial Malware Samples—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