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AI-Powered Network Anomaly Detection & Response System (AINADR)

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d442629

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Posts: 8

5/13/2025 6:24 AM

Build an AI model that monitors network traffic in real-time (simulated via GNS3), detects anomalies (like DDoS, unauthorized access, etc.), and automatically responds by updating firewall rules, blocking IPs, or rerouting traffic.

Components:

GNS3 Network Setup

Simulate a small enterprise network:
Routers
Switches
End devices (PCs, servers)
Firewall (e.g., Cisco ASA or Linux-based)
Traffic generator (using Iperf, Python scripts, etc.)

Traffic Monitoring Tool

Use tcpdump , Wireshark CLI , or TSHARK to capture live traffic.
Forward packet data to your AI model for analysis.

AI/ML Model

Train a model (e.g., using Python + Scikit-learn, TensorFlow, or PyTorch) to classify normal vs abnormal traffic.
Features extracted from traffic:
Packet size
Flow duration
Protocol type
Source/Destination IP frequency
Port scanning patterns

You can use datasets like CICIDS2017 , UNSW-NB15 , or KDD Cup 99 (with modernization).

Integration Layer

Use Python scripts to:
Read live traffic captures
Feed them into the AI model
Trigger responses if anomalies are detected

Automated Response Engine

Based on AI decisions, send commands to:
Block IPs via Cisco ACLs
Modify routing (e.g., redirect traffic via IPS)
Send alerts via email/SMS

Workflow Example:

Run GNS3 topology with simulated user traffic.
Capture packets using tcpdump on key links.
Send packets to Python script → extract features → pass to ML model.
If anomaly detected (e.g., high volume SYN packets = DDoS):
Script sends command via Telnet/SSH/API to router/firewall.
Adds ACL rule to block source IP.
Log event and notify admin.

Tools & Technologies:

COMPONENT
TOOLS
Network Simulation
GNS3, Cisco IOS images, QEMU VMs
Traffic Capture
tcpdump, TSHARK
AI/ML
Python, Scikit-learn, TensorFlow, Pandas
Automation
Python, Paramiko (for SSH), RESTCONF/YANG
Logging/Alerting
ELK Stack, Grafana, Email via SMTP

Bonus Enhancements:

Use Rasa or Chatbot NLU for admin interaction.
Integrate Snort or Suricata as IDS and correlate alerts.
Deploy the AI model on a Docker container inside GNS3.
Add reinforcement learning to optimize firewall policies over time.

Why This Is Cool:

Combines networking (GNS3) with AI/ML and automation
Real-world application: Cybersecurity, NOC automation
Great for portfolios, final-year projects, or demos

Would you like me to provide a sample Python script , GNS3 topology setup guide , or ML model training code to get started?

WATCH WATCH WATCH WATCH WATCH WATCH WATCH WATCH WATCH WATCH WATCH WATCH WATCH

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