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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/deqangss/pad4amd
공식 코드PAD: Towards Principled Adversarial Malware Detection Against Evasion Attacks
https://github.com/dovermore/randomized-deletion
공식 코드RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion
https://github.com/dtrizna/nebula
공식 코드https://github.com/eurecom-s3/decodingmlsecretsofwindowsmalwareclassification
공식 코드https://github.com/fabriciojoc/fast-furious-malware-data-streams
공식 코드Fast & Furious: Modelling Malware Detection as Evolving Data Streams
https://github.com/foreverzyh/defend_framework
공식 코드https://github.com/fra31/sparse-rs
공식 코드Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks
https://github.com/futurecomputing4ai/ember2024
공식 코드EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers
https://github.com/futurecomputing4ai/vtpipeline-rs
공식 코드EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers
https://github.com/gnipping/advdroidzero-access-instructions
공식 코드Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting
https://github.com/gtownrocks/grafuple
공식 코드Applications of Graph Integration to Function Comparison and Malware Classification
https://github.com/guelfoweb/peframe
공식 코드https://github.com/hamidbostani2021/evadedroid
공식 코드EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection
https://github.com/hamidbostani2021/robust-feature-space
공식 코드https://github.com/harelber/MaMaDroid2.0
공식 코드https://github.com/iqsec-lab/lamda
공식 코드LAMDA: A Longitudinal Android Malware Benchmark for Concept Drift Analysis
https://github.com/jaromiru/rcwcf
공식 코드Classification with Costly Features in Hierarchical Deep Sets
https://github.com/joddiy/DynamicMalwareAnalysis
공식 코드Dynamic Malware Analysis with Feature Engineering and Feature Learning
https://github.com/jvss2/machine-learning-empirical-evaluation
공식 코드Imbalanced malware classification: an approach based on dynamic classifier selection
https://github.com/kevinkoo001/ropf
공식 코드https://github.com/malwarereplaygan/malcl
공식 코드https://github.com/marcuscarpenter97/malware-data
공식 코드https://github.com/matouskozak/amg
공식 코드https://github.com/mhunt-er/benchmarking-malware-family-classification
공식 코드A Comprehensive Study on Learning-Based PE Malware Family Classification Methods
https://github.com/mostafa-ja/sigma-binary
공식 코드Evaluating the Robustness of Adversarial Defenses in Malware Detection Systems
https://github.com/msrocean/continual-learning-malware
공식 코드On the Limitations of Continual Learning for Malware Classification
https://github.com/networkgeometry/hyperbolic-benchmark-gnn
공식 코드Hyperbolic Benchmarking Unveils Network Topology-Feature Relationship in GNN Performance
https://github.com/neuromorphiccomputationresearchprogram/hrrformer
공식 코드Recasting Self-Attention with Holographic Reduced Representations
https://github.com/ocatak/malware_api_class
공식 코드Data Augmentation Based Malware Detection using Convolutional Neural Networks
https://github.com/officialarijit/dw-fedavg
공식 코드A Dynamic Weighted Federated Learning for Android Malware Classification
https://github.com/pratikpv/malware_classification
공식 코드https://github.com/pratikpv/malware_detect2
공식 코드An Empirical Analysis of Image-Based Learning Techniques for Malware Classification
https://github.com/probabilistic-jsmas/probabilistic-jsmas
공식 코드https://github.com/pwwl/enhanced-binary-diversification
공식 코드Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes
https://github.com/ryderling/adversarial-attacks-and-defenses-for-windows-pe-malware-detection
공식 코드Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.