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/danielgibert/mlw_classification_structural_entropy
https://github.com/dmlc/dgl/tree/master/examples/pytorch/node2vec
https://github.com/dtrizna/quo.vadis
https://github.com/ee17b031-iittp/MNIST-Retrain-with-DeepXplore
DeepXplore: Automated Whitebox Testing of Deep Learning Systems
https://github.com/eliorc/node2vec
https://github.com/endgameinc/malware_evasion_competition
https://github.com/fahrbach/icml-2020-faster-graph-embeddings
https://github.com/fiona-lxd/AdvKnn
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning
https://github.com/h1n1ron/Microsoft-Malware-Classification
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
https://github.com/heenasingh1995/Microsoft-Malware-Detection-problem
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
https://github.com/iBibek/MalConv-Deep-learning-for-PE-malware-classification
https://github.com/iboutsikas/montemutacon
Evading Malware Classifiers via Monte Carlo Mutant Feature Discovery
https://github.com/icd-codex/icd-codex
https://github.com/jaketae/deep-malware-detection
Learning the PE Header, Malware Detection with Minimal Domain Knowledge
https://github.com/jaketae/pytorch-malware-detection
Learning the PE Header, Malware Detection with Minimal Domain Knowledge
https://github.com/kuronosec/arhuaco
Arhuaco: Deep Learning and Isolation Based Security for Distributed High-Throughput Computing
https://github.com/lions-epfl/lipslev
RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion
https://github.com/malan-project/malan
A Convolutional Transformation Network for Malware Classification
https://github.com/manish-vi/Malware-Detection
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
https://github.com/max-andr/square-attack
Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks
https://github.com/mitchfwx/ISA480
https://github.com/mldroid/subgraph2vec
subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs
https://github.com/mldroid/subgraph2vec_gensim
subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs
https://github.com/mldroid/subgraph2vec_tf
subgraph2vec: Learning Distributed Representations of Rooted Sub-graphs from Large Graphs
https://github.com/monarch-initiative/embiggen
https://github.com/nahmiasd/DeepSign
DeepSign: Deep Learning for Automatic Malware Signature Generation and Classification
https://github.com/olety/TIMLinUCB
https://github.com/owenps/Adversarial_Generator
DeepXplore: Automated Whitebox Testing of Deep Learning Systems
https://github.com/paulmorio/geo2dr
https://github.com/peck94/cann-detector
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning
https://github.com/peikexin9/deepxplore
DeepXplore: Automated Whitebox Testing of Deep Learning Systems
https://github.com/pralab/secml_malware
https://github.com/pralab/toucanstrike
https://github.com/prasanna4567/COVID-19_MLDM
https://github.com/printfer/CS6490_NetworkSecurity_FinalProject
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.