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/razrLeLe/fastwalk
https://github.com/rodgzilla/machine_learning_deep_knn
Deep k-Nearest Neighbors: Towards Confident, Interpretable and Robust Deep Learning
https://github.com/rusty1s/pytorch_cluster
https://github.com/shashank3009/malware
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
https://github.com/shenweichen/GraphEmbedding
https://github.com/shrtCKT/opennet
Learning a Neural-network-based Representation for Open Set Recognition
https://github.com/skyguy19/tdamalwaredetection
Reliable Malware Analysis and Detection using Topology Data Analysis
https://github.com/sophos-ai/SOREL-20M
Automatic Malware Description via Attribute Tagging and Similarity Embedding
https://github.com/sophos/sorel-20m
Automatic Malware Description via Attribute Tagging and Similarity Embedding
https://github.com/soumavaghosh/graph2vec
https://github.com/sujitjean/Microsoft-Malware-Detection
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
https://github.com/tcwangshiqi-columbia/Neurify
https://github.com/tcwangshiqi-columbia/ReluVal
https://github.com/thibaudmartinez/node2vec
https://github.com/tychen5/sportslottery
DeepSign: Deep Learning for Automatic Malware Signature Generation and Classification
https://github.com/urielsinger/ctdne
https://github.com/urielsinger/fairwalk
https://github.com/vinayakumarr/Deep-Net
https://github.com/vkm007/Microsoft-Malware-Detection
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
https://github.com/wizard-kv/Microsoft-Malware-Detection
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
https://github.com/xiaojunxu/dnn-binary-code-similarity
Neural Network-based Graph Embedding for Cross-Platform Binary Code Similarity Detection
https://github.com/yanminglai/Malware-GAN
Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
https://github.com/yortyj/Exploiting-GANs-for-Phun-and-Profit
Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.