01
The Challenge
Most network intrusion detection systems require labeled training data — a luxury unavailable in zero-day attack scenarios. The challenge: detect novel attacks in real-time without any pre-labeled examples.
02
The Approach
Implemented the Kitsune architecture (NDSS'18) using ensemble autoencoders that learn the distribution of normal traffic and flag statistical outliers — completely unsupervised with online learning capabilities.
03
The Solution
- AfterImage feature extractor for real-time network statistics
- KitNET ensemble of autoencoders for anomaly scoring
- Online learning: model updates continuously without batch retraining
- Zero labeled data requirement — fully unsupervised detection
04
The Impact
0 Labels Required
0% Detection Rate
0% Online Learning
0 Pass Required
Tech Stack
Python Autoencoders Machine Learning Network Security Scapy
“The future of intrusion detection is unsupervised — systems that learn normal behavior and detect anomalies without ever seeing an attack example.”