https://github.com/futurecomputing4ai/vtpipeline-rs
공식 코드EMBER2024 -- A Benchmark Dataset for Holistic Evaluation of Malware Classifiers
Papers with Code 아카이브
Malware Detection
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
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Malware Detection is a significant part of endpoint security including workstations, servers, cloud instances, and mobile devices. Malware Detection is used to detect and identify malicious activities caused by malware. With the increase in the variety of malware activities on CMS based websites such as malicious malware redirects on WordPress site (Aka, WordPress Malware Redirect Hack) where the site redirects to spam, being the most widespread, the need for automatic detection and classifier amplifies as well. The signature-based Malware Detection system is commonly used for existing malware that has a signature but it is not suitable for unknown malware or zero-day malware
Source: The Threat of Adversarial Attacks on Machine Learning in Network Security - A Survey
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
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/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/marcuscarpenter97/malware-data
공식 코드https://github.com/matouskozak/amg
공식 코드https://github.com/mostafa-ja/sigma-binary
공식 코드Evaluating the Robustness of Adversarial Defenses in Malware Detection Systems
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/pratikpv/malware_classification
공식 코드https://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
https://github.com/s2labres/autorobust
공식 코드How to Train your Antivirus: RL-based Hardening through the Problem-Space
https://github.com/sachith500/sherlock
공식 코드https://github.com/safreita1/malnet-image
공식 코드https://github.com/serval-uni-lu/realistic_adversarial_hardening
공식 코드https://github.com/seu-proactivesecurity-group/malpurifier
공식 코드https://github.com/shinington/Robust-PDF-Classifier-with-Conserved-Features
공식 코드Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved Features
https://github.com/shoumiksaha/drsm
공식 코드DRSM: De-Randomized Smoothing on Malware Classifier Providing Certified Robustness
https://github.com/shrezaei/MultitaskTrafficClassification
공식 코드https://github.com/star-ailab/armor
공식 코드Adversarially Robust Learning with Optimal Transport Regularized Divergences
https://github.com/stratosphereips/meme_malware_rl
공식 코드The Power of MEME: Adversarial Malware Creation with Model-Based Reinforcement Learning
https://github.com/stratosphereips/model_extraction_malware
공식 코드Stealing and Evading Malware Classifiers and Antivirus at Low False Positive Conditions
https://github.com/surrealyz/verified-global-properties
공식 코드Learning Security Classifiers with Verified Global Robustness Properties
https://github.com/trustworthy-software/detectbert
공식 코드DetectBERT: Towards Full App-Level Representation Learning to Detect Android Malware
https://github.com/trustworthy-software/dexray
공식 코드https://github.com/wagner-group/active-learning
공식 코드https://github.com/wubozhi/Xmal
공식 코드Why an Android App is Classified as Malware? Towards Malware Classification Interpretation
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.