A flow‑based Network Intrusion Detection System using machine learning to accurately identify cyber attacks

The goal of this project is to develop an AI-powered Network Intrusion Detection System (NIDS) capable of identifying malicious network trafic and cyber-attacks in real time. By leveraging machine learning techniques, the system will classify trafic as normal or suspicious based on historical data.

AI-powered Network Intrusion Detection System using Flask and Machine Learning for real-time traffic analysis and threat detection.

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The goal of this project is to develop an Al-powered Network Intrusion Detection System (NIDS) capable of identifying malicious network trafic and cyber-attacks in real time. By leveraging machine learning techniques, the system will classify trafic as normal or suspicious based on historical data. The tool will process network trafic data.

The goal of this project is to develop an AI-powered Network Intrusion Detection System (NIDS) capable of identifying malicious network trafic and cyber-attacks in real time. By leveraging machine learning techniques, the system will classify traffic as normal or suspicious based on historical data.

This project is about the DDOS attack detection using AI and ML which is silently involves in Cyber Security domain too.

No description available

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AI-powered Network Intrusion Detection System (NIDS) that uses Machine Learning to detect and classify malicious network traffic in real time. It processes network data, extracts features, trains models (Decision Tree, Random Forest, SVM), detects anomalies, and generates alerts for potential cyber threats.

SentinelNet NIDS: Engineered a production-ready, three-tier Network Intusion Detection System integrating a C++/Qt client, a Python/Flask ML API, and a PostgreSQL database. This project showcases my skills in machine learning (Scikit-learn), cybersecurity principles, REST APIs, and building asynchronous, real-time monitoring dashboards.