Cybersecurity
Network Intrusion Detection System
Machine-learning anomaly detection over live network traffic
- Python
- Scapy
- XGBoost
- Kafka
- +2
Cybersecurity · Capstone
Ryu controller that classifies flows and installs mitigation rules
Overview
An emulated network in Mininet is driven by a Ryu OpenFlow controller that samples per-flow statistics and feeds them to a classifier trained to separate normal traffic from volumetric floods. When an attack is detected the controller pushes flow rules to rate-limit or drop the offending traffic, and the whole loop runs on virtual hosts you control.
What makes it stand up
Module breakdown
Builds a Mininet topology with hosts, switches and links.
Polls switch flow tables for packet, byte and duration counts.
Trains and evaluates a flow-level attack detector offline.
Installs OpenFlow rules that throttle or block flagged flows.
Compares legitimate throughput with and without defence.
After this, you will be able to
Technology stack
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Cybersecurity
Machine-learning anomaly detection over live network traffic