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 26–50 of 146 papers

TitleStatusHype
A multi-task learning model for malware classification with useful file access pattern from API call sequence—0
A Comparison of Graph Neural Networks for Malware Classification—0
Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks—0
A Malware Classification Survey on Adversarial Attacks and Defences—0
Dynamic Malware Classification of Windows PE Files using CNNs and Greyscale Images Derived from Runtime API Call Argument Conversion—0
Effectiveness of Adversarial Examples and Defenses for Malware Classification—0
Enhancing Efficiency and Privacy in Memory-Based Malware Classification through Feature Selection—0
Deep Learning-Driven Malware Classification with API Call Sequence Analysis and Concept Drift Handling—0
A Survey of Machine Learning Methods and Challenges for Windows Malware Classification—0
Detection of Advanced Malware by Machine Learning Techniques—0
A short review on Applications of Deep learning for Cyber security—0
A Hierarchical Convolutional Neural Network for Malware Classification—0
Defending Malware Classification Networks Against Adversarial Perturbations with Non-Negative Weight Restrictions—0
Designing Deep Convolutional Neural Networks using a Genetic Algorithm for Image-based Malware Classification—0
Detection under Privileged Information—0
Data Augmentation for Opcode Sequence Based Malware Detection—0
Computer activity learning from system call time series—0
Comprehensive Efficiency Analysis of Machine Learning Algorithms for Developing Hardware-Based Cybersecurity Countermeasures—0
Deep Learning and Open Set Malware Classification: A Survey—0
A Novel Feature Representation for Malware Classification—0
Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection—0
AuthAttLyzer-V2: Unveiling Code Authorship Attribution using Enhanced Ensemble Learning Models & Generating Benchmark Dataset—0
Deep-Net: Deep Neural Network for Cyber Security Use Cases—0
Backdooring Explainable Machine Learning—0
CNN vs ELM for Image-Based Malware Classification—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