Attack detection

Inspiration

The increasing number of devastating cross-chain bridge hacks, like the Nomad and Wormhole exploits, highlighted critical security gaps in blockchain interoperability. We were motivated to explore how proactive detection could mitigate these attacks and help secure multi-chain ecosystems.

What it does

Our solution continuously monitors cross-chain transactions and contract interactions to detect suspicious patterns and known exploit signatures. It leverages anomaly detection and rule-based checks to flag potentially malicious activity, providing alerts before damage escalates.

How we built it

We combined: A transaction crawler that listens to cross-chain events (via public RPC endpoints or blockchain indexers). A simple ML-based anomaly detection model trained on historical transaction features. A rules engine to detect known exploit behaviors (like replay attacks, unexpected contract calls, and suspicious validator signatures). A dashboard built with Streamlit for real-time alerts and visualization.

Challenges we ran into

Handling inconsistent data schemas across different chains. Gathering enough labeled attack data to train detection models. Managing rate limits and performance while monitoring multiple chains simultaneously.

Accomplishments that we're proud of

Successfully demonstrated detection of forged cross-chain messages in a simulated environment. Built a modular design so it can be extended to new chains or plugged into security dashboards. Created clear visual summaries that even non-technical stakeholders can understand.

What we learned

The sheer diversity of cross-chain protocols makes universal security extremely challenging. Many cross-chain attacks exploit simple overlooked assumptions, like trusting an external validator set without robust cryptographic guarantees. Cross-chain threat modeling requires thinking beyond individual chain security.

What's next for Attack detection

Integrate more advanced anomaly detection (e.g. graph-based transaction flow analysis). Expand to support additional chains like Polkadot and Cosmos. Collaborate with blockchain bridges to provide a plug-in security advisory module.

Built With

• docker
• ethers.js
• postgresql
• python
• scikit-learn
• streamlit
• web3