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 101–146 of 146 papers

TitleStatusHype
A Comparison of Graph Neural Networks for Malware Classification—0
A Comparison of Word2Vec, HMM2Vec, and PCA2Vec for Malware Classification—0
Adversarial Perturbations Against Deep Neural Networks for Malware Classification—0
A Hierarchical Convolutional Neural Network for Malware Classification—0
A Malware Classification Survey on Adversarial Attacks and Defences—0
A multi-task learning model for malware classification with useful file access pattern from API call sequence—0
A Natural Language Processing Approach to Malware Classification—0
An End-to-End Deep Learning Architecture for Classification of Malware’s Binary Content—0
A Novel Feature Representation for Malware Classification—0
A short review on Applications of Deep learning for Cyber security—0
A Survey of Machine Learning Methods and Challenges for Windows Malware Classification—0
AuthAttLyzer-V2: Unveiling Code Authorship Attribution using Enhanced Ensemble Learning Models & Generating Benchmark Dataset—0
Backdooring Explainable Machine Learning—0
Bayesian Deep Learning for Graphs—0
Behavioral Malware Classification using Convolutional Recurrent Neural Networks—0
Can Feature Engineering Help Quantum Machine Learning for Malware Detection?—0
Can't Boil This Frog: Robustness of Online-Trained Autoencoder-Based Anomaly Detectors to Adversarial Poisoning Attacks—0
Case Study-Based Approach of Quantum Machine Learning in Cybersecurity: Quantum Support Vector Machine for Malware Classification and Protection—0
Classifying Malware Images with Convolutional Neural Network Models—0
Classifying Malware Using Function Representations in a Static Call Graph—0
Cluster Analysis and Concept Drift Detection in Malware—0
CNN-LSTM and Transfer Learning Models for Malware Classification based on Opcodes and API Calls—0
CNN vs ELM for Image-Based Malware Classification—0
Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection—0
Comprehensive Efficiency Analysis of Machine Learning Algorithms for Developing Hardware-Based Cybersecurity Countermeasures—0
Computer activity learning from system call time series—0
Data Augmentation for Opcode Sequence Based Malware Detection—0
Deep Learning and Open Set Malware Classification: A Survey—0
Deep Learning-Driven Malware Classification with API Call Sequence Analysis and Concept Drift Handling—0
Deep-Net: Deep Neural Network for Cyber Security Use Cases—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 of Advanced Malware by Machine Learning Techniques—0
Detection under Privileged Information—0
DRo: A data-scarce mechanism to revolutionize the performance of Deep Learning based Security Systems—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
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
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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