Malware Detection
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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/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/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/printfer/CS6490_NetworkSecurity_FinalProject
https://github.com/probabilistic-jsma/probabilistic-jsma
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/shenweichen/GraphEmbedding
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/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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.