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Intrusion Detection

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하위 태스크: 1
평가표: 6
보존된 평가 기록: 7

Intrusion Detection is the process of dynamically monitoring events occurring in a computer system or network, analyzing them for signs of possible incidents and often interdicting the unauthorized access. This is typically accomplished by automatically collecting information from a variety of systems and network sources, and then analyzing the information for possible security problems.

Source: Machine Learning Techniques for Intrusion Detection

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/AFAgarap/malware-classification

A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data

https://github.com/AFAgarap/wisconsin-breast-cancer

A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data

https://github.com/AlperKaan35/SDN-Dataset

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/MJafarMashhadi/Haplophysh

eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry Keys

https://github.com/MillendRoy/one-shot-reinforcement-learning-classification

Deep Reinforcement One-Shot Learning for Artificially Intelligent Classification Systems

https://github.com/Mind23-2/MindCode-111

eXpose: A Character-Level Convolutional Neural Network with Embeddings For Detecting Malicious URLs, File Paths and Registry Keys

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/antoinedelplace/Cyberattack-Detection

Cyber Attack Detection thanks to Machine Learning Algorithms

https://github.com/cheng0719/E-GraphSAGE_NIDS

E-GraphSAGE: A Graph Neural Network based Intrusion Detection System for IoT

https://github.com/da-moon/classifiers-monorepo

A Neural Network Architecture Combining Gated Recurrent Unit (GRU) and Support Vector Machine (SVM) for Intrusion Detection in Network Traffic Data

https://github.com/dodo47/cyberml

Machine learning on knowledge graphs for context-aware security monitoring

https://github.com/dongtsi/TrafficManipulator

Evaluating and Improving Adversarial Robustness of Machine Learning-Based Network Intrusion Detectors

https://github.com/fisher85/ml-cybersecurity/tree/master/python-web-attack-detection

https://github.com/jackaduma/NLP4CyberSecurity

Cyber Attack Detection thanks to Machine Learning Algorithms

https://github.com/kuronosec/arhuaco

Arhuaco: Deep Learning and Isolation Based Security for Distributed High-Throughput Computing

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/luigiluz/cnn-ids-for-avtp-networks-reproduction

Convolutional Neural Network-based Intrusion Detection System for AVTP Streams in Automotive Ethernet-based Networks

https://github.com/lunpin1101/dablog

Recomposition vs. Prediction: A Novel Anomaly Detection for Discrete Events Based On Autoencoder

https://github.com/margitantal68/mouse_dynamics_balabit_chaoshen_dfl

Intrusion Detection Using Mouse Dynamics

https://github.com/mhwong2007/LuNet

LuNet: A Deep Neural Network for Network Intrusion Detection

https://github.com/n-akram/SafeML

SafeML: Safety Monitoring of Machine Learning Classifiers through Statistical Difference Measure

https://github.com/pfmarteau/DiFF-RF

Random Partitioning Forest for Point-Wise and Collective Anomaly Detection -- Application to Intrusion Detection

https://github.com/pfmarteau/HIF

Hybrid Isolation Forest - Application to Intrusion Detection

https://github.com/pfmarteau/STree4CS

Sequence Covering for Efficient Host-Based Intrusion Detection

https://github.com/prximenes/2D-CNN-IDS-FOR-IVNs

Convolutional Neural Network-based Intrusion Detection System for AVTP Streams in Automotive Ethernet-based Networks

https://github.com/pygod-team/pygod

AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.