https://github.com/dino-chiio/fedmse
공식 코드FedMSE: Federated learning for IoT network intrusion detection
Papers with Code 아카이브
Intrusion Detection
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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.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/donghao51/nng-mix
공식 코드NNG-Mix: Improving Semi-supervised Anomaly Detection with Pseudo-anomaly Generation
https://github.com/dreizehnutters/pcapae
공식 코드Representation Learning for Content-Sensitive Anomaly Detection in Industrial Networks
https://github.com/dtolpin/rmi-case-studies
공식 코드https://github.com/dual-grp/fedpca_anomalydetection
공식 코드Federated PCA on Grassmann Manifold for IoT Anomaly Detection
https://github.com/freddyAdjh/Federated-security
공식 코드Efficient Federated Intrusion Detection in 5G ecosystem using optimized BERT-based model
https://github.com/ghosh64/transferability
공식 코드A Study on Transferability of Deep Learning Models for Network Intrusion Detection
https://github.com/haoyfan/AnomalyDAE
공식 코드AnomalyDAE: Dual autoencoder for anomaly detection on attributed networks
https://github.com/hihey54/pragmaticassessment
공식 코드SoK: Pragmatic Assessment of Machine Learning for Network Intrusion Detection
https://github.com/hiyouga/llama-efficient-tuning
공식 코드Large Language Models for Cyber Security: A Systematic Literature Review
https://github.com/hust-iof/efficient-dvs
공식 코드https://github.com/intrudetection/robevalanodetect
공식 코드Robustness Evaluation of Deep Unsupervised Learning Algorithms for Intrusion Detection Systems
https://github.com/kahramankostas/IPF
공식 코드Individual Packet Features are a Risk to Model Generalisation in ML-Based Intrusion Detection
https://github.com/kahramankostas/IoTGeM
공식 코드IoTGeM: Generalizable Models for Behaviour-Based IoT Attack Detection
https://github.com/kahramankostas/heriot
공식 코드IoTGeM: Generalizable Models for Behaviour-Based IoT Attack Detection
https://github.com/kiymetkaya/xai-catboosted-anomale
공식 코드X-CBA: Explainability Aided CatBoosted Anomal-E for Intrusion Detection System
https://github.com/koukipp/contrastive_transformers_ids
공식 코드Self-Supervised Transformer-based Contrastive Learning for Intrusion Detection Systems
https://github.com/labsaint/tsi-gan
공식 코드https://github.com/lamsieh/DQN
공식 코드Deep Q-Learning based Reinforcement Learning Approach for Network Intrusion Detection
https://github.com/liamdm/flowtransformer
공식 코드FlowTransformer: A Transformer Framework for Flow-based Network Intrusion Detection Systems
https://github.com/mamerzouk/adversarial-purification
공식 코드Diffusion-based Adversarial Purification for Intrusion Detection
https://github.com/marcocantone/lycos-unicas-ids2018
공식 코드On the Cross-Dataset Generalization of Machine Learning for Network Intrusion Detection
https://github.com/marcuscarpenter97/malware-data
공식 코드https://github.com/martaandronic/polylut
공식 코드PolyLUT: Learning Piecewise Polynomials for Ultra-Low Latency FPGA LUT-based Inference
https://github.com/ogarreche/xai_feature_selection
공식 코드XAI-based Feature Selection for Improved Network Intrusion Detection Systems
https://github.com/ogarreche/xai_whitebox
공식 코드A Comparative Analysis of DNN-based White-Box Explainable AI Methods in Network Security
https://github.com/oidebrett/mcpauth
공식 코드Simplified and Secure MCP Gateways for Enterprise AI Integration
https://github.com/othmbela/dbn-based-nids
공식 코드https://github.com/othmbela/gotham-network-packet-labeller
공식 코드https://github.com/pajola/xenids
공식 코드The Cross-evaluation of Machine Learning-based Network Intrusion Detection Systems
https://github.com/pcwhy/xml-intrusiondetection
공식 코드Explainable AI for Comparative Analysis of Intrusion Detection Models
https://github.com/pingayush/INTELLIGUARD-Enhancing-Network-Security
공식 코드https://github.com/pmoriano/benchmarking-unsupervised-online-IDS-masquerade-attacks
공식 코드Benchmarking Unsupervised Online IDS for Masquerade Attacks in CAN
https://github.com/provenanceanalytics/kairos
공식 코드Kairos: Practical Intrusion Detection and Investigation using Whole-system Provenance
https://github.com/qcydm/dids-mfl
공식 코드https://github.com/racsa-lab/EDD
공식 코드Edge-Detect: Edge-centric Network Intrusion Detection using Deep Neural Network
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.