cicids2017

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.

Network Intrusion Detection System using Machine Learning and Deep Learning

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

ThreatGuard is an advanced threat detection system that utilizes the CICIDS 2017 dataset for network traffic analysis and anomaly detection.

Reproducible ML-based network intrusion detection pipeline using CICIDS2017 and UNSW-NB15 datasets.

CyberSentinel AI - Zero Trust Decision Engine built on CIC-IDS2017 with production-grade ML pipeline, FastAPI inference service, and real-time security analytics dashboard.

Predicts network intrusion severity using the CICIDS2017 dataset. Utilizes deep learning to classify PCAP-extracted features into risk-based severity tiers rather than standard binary detection.

"A large-scale real-world network perimeter threat dataset with verified labels"

Building a Machine Learning-based NIDS using XGBoost trained on the CICIDS2017 dataset. 🚀

AI-powered security alert triage agent with RAG-grounded remediation.

AI-based detection of cyber threats in unidirectional IP traffic - ML-powered IDS sensor for data-diode networks (CIC-IDS2017, XGBoost) with live dashboard

AI Network Auditor: AI-security swarm verdicts that cut IDS false positives with signed audit trails — defends against AI attacks, governs AI agents

Full-stack ML intrusion detection system. Random Forest + Stacking Classifier on CIC-IDS2017 (78 features). FastAPI, async PostgreSQL, real-time WebSocket dashboard, Prometheus metrics, MLflow tracking, API key auth.

CICIDS2017 Random Forest flow classifier with Flask dashboard and transparent scope boundaries.

Compilação de cabeçalhos de regras do Snort 3 em pré-filtros P4 conservadores, avaliada no BMv2: artigo, compilador, programa P4 e artefatos experimentais.

Machine learning based DDoS detection system using CICIDS2017, Streamlit and scikit-learn

선박·산업 장비 환경의 네트워크 flow 데이터를 분석해 이상징후를 탐지하고, 실시간 대시보드로 확인·관리하는 모니터링 포트폴리오입니다.

AI-Based Cyber Threat Detection Framework using Machine Learning, Python, Streamlit, and the CICIDS2017 dataset for intelligent network intrusion detection and threat classification.

Hybrid AI-powered Intrusion Detection System (NIDS) combining 1D-CNN & Variational Autoencoder (VAE) to detect known cyberattacks and zero-day anomalies. Features a premium Streamlit "Command Center" dashboard for real-time network traffic analysis.

Real-time network intrusion detection system with two-stage ML (Isolation Forest + XGBoost), open-set escalation, SHAP explainability, Tier 2 LLM/RAG reasoning, and a live React dashboard.