Welcome this is a comprehensive repository dedicated to advancing Network Intrusion Detection Systems (NIDS) through the power of Machine Learning (ML) and Deep Learning (DL). This project aims to develop, evaluate, and optimize intelligent models capable of accurately detecting and mitigating a wide array of network threats and anomalies.

This tool is a TypeScript-based application designed to parse and check emails in Google and Outlook email IDs, respond to emails based on context using AI, and use BullMQ as the task scheduler. It integrates with OpenAI for email context analysis and response generation

AI - based (Machine Learning) IDS using XGBoost and Flask API

🚀 Next.js SaaS example that shows a real AI credit‑based product: anonymous chat until sign‑in, Better Auth for users, Prisma for data, Freemius for subscriptions, one‑off credit top‑ups, trials, entitlements, and a customer portal. Clone it, add your Freemius pricing IDs + API keys, run migrations, and start selling. Go Build 🚀

This is a signature based IDS developed using AI, TF, ML and keras.

The ultimate workbench for research & development of AI-powered anomaly-based Intrusion Detection Systems (IDS)

SentinelSec is a comprehensive, offline-first Intrusion Detection System (IDS) built with Python. It combines real-time packet monitoring, AI-based anomaly detection, CVE vulnerability intelligence, and rule-based threat detection in a single, powerful platform.

An AI-driven Network Intrusion Detection System utilizing CNN and LSTM models trained on the UNSW-NB15 dataset to detect and classify malicious traffic with high precision. It features a web-based dashboard for real-time monitoring, offering both binary and multi-class classification to secure networks against diverse cyber threats.

Explainable AI-based IDS in IoT networks

This is an advanced Intrusion Detection System (IDS) that leverages Artificial Intelligence (AI) and Snort to detect network anomalies in real time. The system is designed to enhance cybersecurity by identifying malicious traffic, unauthorized access, and potential cyber threats using machine learning models and signature-based detection.

is a sophisticated AI-driven rule-based Intrusion Detection System (IDS) designed to detect malicious behavior in network traffic

Cortex IDS is a professional-grade, real-time Intrusion Detection & Prevention System (IDPS) designed for Windows environments. It leverages Machine Learning (Random Forest) to analyze network traffic patterns and distinguish between legitimate user activity and malicious cyber threats (DDoS, Port Scanning, Brute Force).

🛡️ Network IDS Python-based IDS with real-time packet capture 🖧, feature extraction 📊, rule-based & AI-enhanced attack detection ⚡. Streamlit dashboard 🌐 shows live traffic, alerts 🚨, and allows threshold adjustment. Modular, extendable, ML-ready 🤖.

An Intrusion Detection System (IDS) monitors network traffic to identify suspicious activity or attacks. An AI-based IDS adds a layer of intelligence—it learns patterns from normal vs. malicious traffic and predicts intrusions automatically.

A lightweight AI config manager for developers. Centralize API Keys, Base URLs, and Model IDs with local DPAPI encryption. Quick-copy credentials and streamline your local debugging workflow without constantly searching through documentation.

A machine learning firewall that doesn’t just block based on rules/signatures (like pfSense, iptables, Snort, Suricata) but learns traffic behavior over time and auto-adapts filtering rules. Instead of being just an IDS (detect only), it acts as an active AI filter.

AI-Based Intrusion Detection System

No description available

This project aims to predict potential security threats in network traffic by analyzing pre-recorded data. Using machine learning techniques, the model processes historical network traffic data to identify patterns and anomalies indicative of cyber threats.

This is an AI based IDS that focuses on testing the effectiveness of different machine learning algorithms on the same dataset, namely the UNSW-NB15 dataset

No description available

No description available

AI-based IDS using CICIDS 2017 dataset

Development of an AI-based IDS on 5G environments

this repository is based on https://github.com/AI-IDS/kdd99_feature_extractor

Lightweight TinyLSTM-based IDS for IoT devices, optimizing memory and energy usage for edge AI deployment.

Project Objectives The objectives of this project are to: 1. Understand security threats in IoT-based CPS environments 2. Study AI-based intrusion detection techniques 3. Design an AI-based IDS model 4. Evaluate the effectiveness of the proposed IDS 5. Analyze system performance and limitations

🌐 Enhance tourist safety with a web-based system featuring real-time monitoring, blockchain IDs, and AI alerts for secure travel experiences.

This project implements an AI-Based Intrusion Detection System (IDS) using the CICIDS 2017 dataset. It performs data preprocessing, machine learning–based classification, alert generation, and SOC-style visualization to support cybersecurity monitoring.

AI-Based Intrusion Detection & Prevention System (IDS/IPS) A real-time cybersecurity system that uses machine learning to detect and block malicious network traffic using Python, Scapy, and Isolation Forest.