Malware Classification
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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
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MindSpore-scientific/code-3/tree/main/Black-Box-Attacks-against
Black-Box Attacks against RNN based Malware Detection Algorithms
https://github.com/NeuromorphicComputationResearchProgram/KiloGrams
https://github.com/Nina-Konovalova/TSP-RL-Skoltech_project
https://github.com/Pathakvishnu/Microsoft-Malware-Detection--Kaggle
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
https://github.com/PrasunDutta007/Microsoft-Malware-Detection-Analysis
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
https://github.com/PrasunDutta007/Microsoft-Malware-Detection-Research-Project-
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
https://github.com/Raman-Raje/Microsoft-Malware-Detecion
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
https://github.com/Raman-Raje/Microsoft-Malware-Detection
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
https://github.com/RiS3-Lab/ModelXRay
https://github.com/Robotmurlock/Deepwalk-and-Node2vec
https://github.com/SamratSengupta/Microsoft-malware-detection
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
https://github.com/Tarunkumar111/Microsoft-Malware-Detection-ML
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
https://github.com/TheJacksonLaboratory/N2V
https://github.com/UMBC-DREAM-Lab/montemutacon
Evading Malware Classifiers via Monte Carlo Mutant Feature Discovery
https://github.com/Vi45en/Pesidious
Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
https://github.com/WahajJaved20/Malware-Classification-Using-Static-Disassembly-and-Machine-Learning
Malware Classification Using Static Disassembly and Machine Learning
https://github.com/WiktorJ/msnode2vec
https://github.com/WilliamBriguglio/Malware-Classifier-Interpreter
Interpreting Machine Learning Malware Detectors Which Leverage N-gram Analysis
https://github.com/alewarne/ExplainSecurityDNNs
Evaluating Explanation Methods for Deep Learning in Security
https://github.com/alewarne/Layerwise-Relevance-Propagation-for-LSTMs
Evaluating Explanation Methods for Deep Learning in Security
https://github.com/alistairwgillespie/deep_dga_detection
Detecting DGA domains with recurrent neural networks and side information
https://github.com/bam098/deep_knn
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning
https://github.com/benedekrozemberczki/graph2vec
https://github.com/benedekrozemberczki/karateclub
https://github.com/bharath7896/Microsoft-malware-detection
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
https://github.com/brechtvandervliet/ResistancePoisoningFederatedMalwareClassifier
Less is More: A privacy-respecting Android malware classifier using Federated Learning
https://github.com/buaayangchun/malwrebackdoor
Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers
https://github.com/cmikke97/Automatic-Malware-Signature-Generation
ALOHA: Auxiliary Loss Optimization for Hypothesis Augmentation
https://github.com/coderatwork7/attack
https://github.com/compnet/pang
https://github.com/cvg/scrstudio
https://github.com/dangmc/Malware
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
https://github.com/danielgibert/mlw_classification_cnn_assembly
https://github.com/danielgibert/mlw_classification_cnn_img
https://github.com/danielgibert/mlw_classification_hydra
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.