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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/rahul110228/VenturasTest

공식 코드

SCGNet-Stacked Convolution with Gated Recurrent Unit Network for Cyber Network Intrusion Detection and Intrusion Type Classification

https://github.com/renj-xu/negsc

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Applying Self-supervised Learning to Network Intrusion Detection for Network Flows with Graph Neural Network

https://github.com/robshahla/VisQUIC

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Exploring QUIC Dynamics: A Large-Scale Dataset for Encrypted Traffic Analysis

https://github.com/s-mohammad-hashemi/repo

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Enhancing Robustness Against Adversarial Examples in Network Intrusion Detection Systems

https://github.com/sandialabs/packet2vec

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Packet2Vec: Utilizing Word2Vec for Feature Extraction in Packet Data

https://github.com/sebavarghese/dt-based-ids-framework

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Digital Twin-based Intrusion Detection for Industrial Control Systems

https://github.com/shahriar0651/canshield

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CANShield: Deep Learning-Based Intrusion Detection Framework for Controller Area Networks at the Signal-Level

https://github.com/shahriar0651/noisec

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Let the Noise Speak: Harnessing Noise for a Unified Defense Against Adversarial and Backdoor Attacks

https://github.com/shinington/Robust-PDF-Classifier-with-Conserved-Features

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Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved Features

https://github.com/sm3a96/ids-machine-learning-techniques-

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A Comprehensive Comparative Study of Individual ML Models and Ensemble Strategies for Network Intrusion Detection Systems

https://github.com/smilies-polito/caracas

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CARACAS: vehiCular ArchitectuRe for detAiled Can Attacks Simulation

https://github.com/taneya1987/crystals-kyber-and-ids

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Secured Communication Schemes for UAVs in 5G: CRYSTALS-Kyber and IDS

https://github.com/tommasofioravanti/sq-learn

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Evaluating the Potential of Quantum Machine Learning in Cybersecurity: A Case-Study on PCA-based Intrusion Detection Systems

https://github.com/tommasopuccetti/rospace_dataset

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ROSpace: Intrusion Detection Dataset for a ROS2-Based Cyber-Physical System

https://github.com/trust-tuda/argus-data

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ARGUS: Context-Based Detection of Stealthy IoT Infiltration Attacks

https://github.com/waimorris/Anomal-E

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Anomal-E: A Self-Supervised Network Intrusion Detection System based on Graph Neural Networks

https://github.com/waimorris/E-GraphSAGE

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

https://github.com/wangkai-tech23/LiPar

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LiPar: A Lightweight Parallel Learning Model for Practical In-Vehicle Network Intrusion Detection

https://github.com/wangkai-tech23/StatGraph

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STATGRAPH: Effective In-vehicle Intrusion Detection via Multi-view Statistical Graph Learning

https://github.com/western-oc2-lab/automl-and-adversarial-attack-defense-for-zero-touch-network-security

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Enabling AutoML for Zero-Touch Network Security: Use-Case Driven Analysis

https://github.com/western-oc2-lab/autonomouscyber-automl-based-autonomous-intrusion-detection-system

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Towards Autonomous Cybersecurity: An Intelligent AutoML Framework for Autonomous Intrusion Detection

https://github.com/western-oc2-lab/cross-layer-autonomous-cybersecurity-framework

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Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networks

https://github.com/western-oc2-lab/intrusion-detection-system-using-cnn-and-transfer-learning

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A Transfer Learning and Optimized CNN Based Intrusion Detection System for Internet of Vehicles

https://github.com/wulongfeng/calirare

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Towards Reliable Rare Category Analysis on Graphs via Individual Calibration

https://github.com/xaiseries/lexnet

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A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classification

https://github.com/xiagll/optiforest

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OptIForest: Optimal Isolation Forest for Anomaly Detection

https://github.com/xinchen930/ssf-strategic-selection-and-forgetting

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Continual Learning with Strategic Selection and Forgetting for Network Intrusion Detection

https://github.com/xinyiys/data_distribution_valuation

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Data Distribution Valuation

https://github.com/yasir-ali-farrukh/gnn4id

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XG-NID: Dual-Modality Network Intrusion Detection using a Heterogeneous Graph Neural Network and Large Language Model

https://github.com/ymirsky/KitNET-py

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Kitsune: An Ensemble of Autoencoders for Online Network Intrusion Detection

https://github.com/yzhao062/SUOD

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SUOD: Accelerating Large-Scale Unsupervised Heterogeneous Outlier Detection

https://github.com/yzhao062/pyod

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PyOD 2: A Python Library for Outlier Detection with LLM-powered Model Selection

https://github.com/yzhao062/pytod

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TOD: GPU-accelerated Outlier Detection via Tensor Operations

https://gitlab.com/compromise3/co-defend

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CO-DEFEND: Continuous Decentralized Federated Learning for Secure DoH-Based Threat Detection

https://bitbucket.org/paulpei/resgcn

ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks

https://github.com/AFAgarap/cnn-svm

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

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