Algorithms that interpret
protocol behavior and
predict security risks
What the six modules actually cover
Protocol architecture parsing
You start by feeding raw smart contract code into parsing tools that separate logic from noise. The model learns which functions carry financial risk.
Transaction pattern recognition
Historical transaction data reveals how users interact with protocols under normal conditions. Outliers suggest either innovation or manipulation.
Anomaly detection systems
Supervised learning on past exploit data trains models to flag suspicious patterns before funds move. Speed matters more than perfect accuracy.
Gas optimization analysis
Models predict which contract modifications reduce transaction costs without compromising security. Small efficiency gains compound across millions of calls.
Risk scoring frameworks
Multiple neural networks evaluate liquidity depth, code complexity, and governance structure. The output is a single risk score that updates in real time.
Simulation environments
Before deploying any model to production, you test it against synthetic attacks in a sandboxed blockchain. Failure here is cheap; failure in production is catastrophic.
Six weeks of remote sessions
Each module runs for one week with recorded lectures, live code reviews, and written assignments. Sessions happen twice weekly in the evening Singapore time.
You get access to a private repository with annotated examples from actual protocol audits. All data is anonymized but the vulnerabilities are real.
Python and blockchain fundamentals
You need working knowledge of Python libraries like pandas and scikit-learn. If you have built a regression model before, you have the foundation.
Understanding Ethereum transaction structure helps but is not required. We cover smart contract basics in the first two sessions before moving into machine learning applications.