cicids2017 开源项目推荐（129 个）

Western-OC2-Lab /Intrusion-Detection-System-Using-Machine-Learning

IDS-ML代码：使用机器学习算法开发入侵检测系统（决策树、随机森林、额外树、XGBoost、堆叠、k-means、Bayesian优化..）

kahramankostas /Anomaly-Detection-in-Networks-Using-Machine-Learning

A thesis submitted for the degree of Master of Science in Computer Networks and Security

Western-OC2-Lab /PWPAE-Concept-Drift-Detection-and-Adaptation

Data stream analytics: Implement online learning methods to address concept drift and model drift in data streams using the River library. Code for the paper entitled "PWPAE: An Ensemble Framework for Concept Drift Adaptation in IoT Data Streams" published in IEEE GlobeCom 2021.

noushinpervez /Intrusion-Detection-CICIDS2017

This repository contains an in-depth analysis of the Intrusion Detection Evaluation Dataset (CIC-IDS2017) for Intrusion Detection, showcasing the implementation and comparison of different machine learning models for binary and multi-class classification tasks.

othmbela /dbn-based-nids

An Intrusion Detection System based on Deep Belief Networks

elifnurkarakoc /CICIDS2017

CICIDS2017 dataset

Western-OC2-Lab /MSANA-Online-Data-Stream-Analytics-And-Concept-Drift-Adaptation

Data stream analytics: Implement online learning methods to address concept drift and model drift in dynamic data streams. Code for the paper entitled "A Multi-Stage Automated Online Network Data Stream Analytics Framework for IIoT Systems" published in IEEE Transactions on Industrial Informatics.

Western-OC2-Lab /Cross-Layer-Autonomous-Cybersecurity-Framework

This repository includes code for the paper "Towards Zero Touch Networks: Cross-Layer Automated Security Solutions for 6G Wireless Networks" published in IEEE TCOM, focusing on autonomous cybersecurity (physical-layer authentication and cross-layer intrusion detection) using AutoML techniques.

Western-OC2-Lab /Multi-Objective-Optimization-AutoML-based-Intrusion-Detection-System

This repository includes code for the paper “Toward Autonomous and Efficient Cybersecurity: A Multi Objective AutoML based Intrusion Detection System” published in IEEE TMLCN, implementing AutoML and MOO-based intrusion detection systems that optimize both ML model effectiveness and efficiency for IoT systems.

manandhar01 /Intrusion_Detection_System

Intrusion Detection System implemented using Few-Shot Learning and CICIDS2017 dataset

aaaastark /Intrusion-Detection-System

Attack Detection, Parameter Optimization and Performance Analysis in Enterprise Networks (ML Networks) for Intrusion Detection System IDS.

vaishnavucv /Project-Network-IDS

Network-Based Intrusion Detection System - dev/deploy-ment

georgeani /NIDS_Dissertation_2021

This repository contains my third year dissertation. My dissertation focused in evaluating and creating a DNN for a Network Intrusion Detection System (NIDS).

MrPotato123540 /Cpp-Network-Anomaly-Detection

Flow-based network intrusion detection in C++, with a live dashboard.

Priyanshu-Ku /NIDS-ML

Network Intrusion Detection System using Machine Learning and Deep Learning

BrenoFariasdaSilva /DDoS-Detector

A Framework for DDoS Attack Detection Using Hyperparameter Optimization, WGAN-GP–Based Data Augmentation, Feature Extraction via Genetic Algorithms, RFE, and PCA, with Individual and Ensemble Classifiers for Multi-Dataset Evaluation.

sadmansakib37 /IDS_DeepLearning_CICIDS2017

Deep learning model comparison for intrusion detection on CIC-IDS2017 dataset

abedellah /SentinelAI

Real-time intrusion detection system: RandomForest on CICIDS2017 (15 attack types), SOC dashboard, polyglot SQL + MongoDB + Redis architecture

Hemanth-Raju-311 /XWF-Agg-Federated-Intrusion-Detection

Novel Explainability-Weighted Federated Aggregation for Privacy-Preserving Network Intrusion Detection using XAI

LiYangHart /MOO-NSGA-III-AutoML-based-Intrusion-Detection-System

This repository includes code for the paper “A Multi-Objective AutoML-based Efficient Intrusion Detection System for EV Charging Networks” accepted in IEEE Global Communications Conference (GLOBECOM 2026), implementing MOO (NSGA-III) and AutoML-based intrusion detection systems that optimize both ML model performance and efficiency for IoT systems.