AI-Driven Cyber Detection System

An AI-powered system that detects and prevents cyber threats in real-time using machine learning and anomaly detection

Comment

With the increasing number of cyberattacks, securing networks has become more critical than ever. Traditional Intrusion Detection Systems (IDS) often struggle with evolving threats and produce too many false positives. To address these challenges, we built an AI-Based Network Intrusion Detection System (NIDS) that leverages machine learning and deep learning to detect anomalies in network traffic.

What Inspired Us?

Cybersecurity threats are growing, and we wanted to create an intelligent system that can learn and adapt to new attack patterns. Inspired by real-world cyber incidents and the need for automated security, we developed this AI-powered solution.

How We Built It?

  1. Data Collection: We used the NSL-KDD dataset and real-time network traffic logs.

  2. Preprocessing: Applied feature selection, normalization, and encoding for ML training.

  3. Model Development: Trained Random Forest, SVM, and Deep Learning models (LSTMs, CNNs) to classify network traffic as normal or malicious.

  4. Deployment: Built an API using Flask/FastAPI, integrated with Snort/Suricata for real-time analysis.

  5. Dashboard: Created a React.js frontend with Grafana for visualization.

  6. Cloud Deployment: Hosted on AWS/GCP/Azure for scalability.

Challenges We Faced

Reducing false positives without missing real attacks.

Handling large-scale network data efficiently.

Deploying a real-time, low-latency AI model for intrusion detection.

Built With

Share this project:

Updates