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Network Intrusion Detection
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Network intrusion detection is the task of monitoring network traffic to and from all devices on a network in order to detect computer attacks.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/s-mohammad-hashemi/repo
공식 코드Enhancing Robustness Against Adversarial Examples in Network Intrusion Detection Systems
https://github.com/sm3a96/ids-machine-learning-techniques-
공식 코드https://github.com/tommasofioravanti/sq-learn
공식 코드https://github.com/waimorris/Anomal-E
공식 코드Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networks
https://github.com/waimorris/E-GraphSAGE
공식 코드E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT
https://github.com/wangkai-tech23/LiPar
공식 코드LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion Detection
https://github.com/wulongfeng/calirare
공식 코드Towards Reliable Rare Category Analysis on Graphs via Individual Calibration
https://github.com/xinchen930/ssf-strategic-selection-and-forgetting
공식 코드Continual Learning with Strategic Selection and Forgetting for Network Intrusion Detection
https://github.com/xinyiys/data_distribution_valuation
공식 코드https://github.com/yasir-ali-farrukh/gnn4id
공식 코드https://github.com/ymirsky/KitNET-py
공식 코드Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection
https://github.com/yzhao062/pyod
공식 코드PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection
https://github.com/George730/E-ResGAT
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT
https://github.com/GuansongPang/deep-outlier-detection
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection
https://github.com/Guillem96/kitsune-pytorch
Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection
https://github.com/Kaslanarian/SAGOD
AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks
https://github.com/QWERTSKIHACK/network-threats-taxonomy
A Taxonomy of Network Threats and the Effect of Current Datasets on Intrusion Detection Systems
https://github.com/Ryosaeba8/Anomaly_detection
Learning Representations of Ultrahigh-dimensional Data for Random Distance-based Outlier Detection
https://github.com/Saurabh2805/kdd_cup_99
Benchmarking datasets for Anomaly-based Network Intrusion Detection: KDD CUP 99 alternatives
https://github.com/Wphackedhelp/PHP-backdoors
https://github.com/Wphackedhelp/Wordpress-scanner
https://github.com/ajaychawda58/SOM_DAGMM
Self-Organizing Map assisted Deep Autoencoding Gaussian Mixture Model for Intrusion Detection
https://github.com/cheng0719/E-GraphSAGE_NIDS
E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT
https://github.com/dongtsi/TrafficManipulator
https://github.com/fisher85/ml-cybersecurity/tree/master/python-web-attack-detection
https://github.com/jackaduma/NLP4CyberSecurity
https://github.com/larsonreever/awesome-malware-analysis
https://github.com/lmunoz-gonzalez/Poisoning-Attacks-with-Back-gradient-Optimization
Detection of Adversarial Training Examples in Poisoning Attacks through Anomaly Detection
https://github.com/mhwong2007/LuNet
LuNet: A Deep Neural Network for Network Intrusion Detection
https://github.com/pfmarteau/HIF
Hybrid Isolation Forest - Application to Intrusion Detection
https://github.com/pygod-team/pygod
AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks
https://github.com/rahulvigneswaran/Intrusion-Detection-Systems
https://github.com/robeespi/Deep-Semi-supervised-intrusion-detection-on-Hadoop-distributed-file-system-log
https://github.com/robeespi/Deep-Semi-supervised-intrusion-detection-on-unstructured-Hadoop-distributed-file-system-logs
https://github.com/robeespi/Weakly-Supervised-Malware-Detection
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.