The problem SPIE solves

The WazirX 2024 hack highlights the urgent need for real-time transaction analysis and wallet monitoring in Web3. Over $230 million was stolen that cost WazirX their reputation and customer trust. Our platform addresses these gaps by offering real-time analytics that detect unusual transaction patterns, cross-wallet interactions, and high-risk activities instantly, enabling protocols to act swiftly and prevent escalation.

Shut Down Risks and Contagion Effects: Without real-time insights, protocols are at risk of undetected breaches that can lead to shutdowns. Decentralized finance operates in an interconnected environment, where breaches in one protocol can trigger a ripple effect, impacting multiple platforms. Our platform mitigates these contagion effects by providing immediate alerts on abnormal wallet interactions and transaction flows, enabling protocols to contain risks and prevent a cascade of security incidents.

Mule Accounts and Anti-Money Laundering (AML) Compliance: A lack of robust monitoring allows mule accounts to obscure the origin and destination of funds, which is increasingly exploited by bad actors in DeFi. Our platform uses real-time detection tools to flag suspicious patterns indicative of mule activity, enhancing protocols' AML compliance and reducing the risk of enabling illicit fund movements. This immediate visibility into transaction origins and behaviors helps prevent fraudulent transactions from slipping through undetected.

Enhanced Security and Market Reputation: Fraud not only leads to asset loss but also damages user trust and protocol reputation, as seen with WazirX. Our platform’s real-time monitoring enables swift fraud detection, safeguarding both finances and reputation. Proactive fraud measures strengthen market credibility and maintain user confidence, crucial in the trust-driven Web3 ecosystem.

Challenges we ran into

During development, we faced multiple challenges that required quick problem-solving, especially under the hackathon’s time constraints.

1. Frequent QuickNode Stream Filter Code Adjustments
The QuickNode stream received major updates from the backend team, which modified the default filter code multiple times. This required us to repeatedly adjust our data access code to align with these changes. To streamline this process, we implemented modular code blocks, allowing us to quickly update specific sections without affecting the entire system, ensuring consistent data accuracy despite frequent adjustments.

2. High Data Influx vs. Processing Speed
The QuickNode stream provided real-time data at a speed higher than our server’s processing capacity, creating a bottleneck. To manage this, we set up a queue and buffering system that held incoming data temporarily, allowing us to process it in batches without losing information.

3. Ethereum RPC Unknown Errors
At times, Ethereum RPC calls returned “Unknown error” messages, lacking specific details, which complicated debugging. We introduced custom error handling and logging mechanisms to capture additional context on each call, helping us detect and address error patterns more efficiently.

4. Ngrok Warnings During Frontend Calls
Ngrok displayed intermittent warning pages during frontend data requests, interrupting access to processed stream data. We resolved this by fine-tuning Ngrok settings and adding exception handling on the frontend, which improved data access and minimized these interruptions.

Despite these challenges, we addressed each one methodically, building a more resilient, responsive system as a result.