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
Enhancing Efficiency and Privacy in Memory-Based Malware Classification through Feature Selection—0
Malware Classification using Deep Neural Networks: Performance Evaluation and Applications in Edge Devices—0
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
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 Novel Feature Representation for Malware Classification—0
Designing Deep Convolutional Neural Networks using a Genetic Algorithm for Image-based Malware Classification—0
AI-based Malware and Ransomware Detection Models—0
Generative Adversarial Networks and Image-Based Malware Classification—0
Representation learning with function call graph transformations for malware open set recognition—0
Backdooring Explainable Machine Learning—0
Malceiver: Perceiver with Hierarchical and Multi-modal Features for Android Malware Detection—0
Bayesian Deep Learning for Graphs—0
Graph Neural Network-based Android Malware Classification with Jumping Knowledge—0
Comprehensive Efficiency Analysis of Machine Learning Algorithms for Developing Hardware-Based Cybersecurity Countermeasures—0
Benchmark Static API Call Datasets for Malware Family Classification—0
Poison Forensics: Traceback of Data Poisoning Attacks in Neural Networks—0
DRo: A data-scarce mechanism to revolutionize the performance of Deep Learning based Security Systems—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