Discussions

Dear GoPlus Team,

I am a blockchain researcher deeply impressed by the innovative approaches GoPlus brings to blockchain security. Your work in phishing and fraud detection is particularly inspiring, and I hope to contribute to this field by exploring how predictive models can complement existing mechanisms.

While I understand that GoPlus currently relies on both user reports and collaborations with monitoring institutions to identify fraudulent activities, my question is:

Has GoPlus considered integrating predictive analytics to proactively identify potential threats before they are flagged by user reports or monitoring institutions?

For example, predictive models could analyze transaction patterns, contract deployments, and on-chain activities to assign a "suspicion score" to new addresses. This could complement your existing frameworks by flagging potential threats earlier, allowing for a more proactive approach.

Here’s a conceptual code snippet illustrating a potential predictive model's integration:

const analyzeAddressRisk = async (address) => {
    const onChainData = await fetchOnChainData(address);
    const riskScore = predictiveModel.calculateRiskScore(onChainData);

    if (riskScore > 0.8) {
        console.log(`High-risk address detected: ${address}`);
        await GoPlus.reportSuspiciousAddress(address);
    } else {
        console.log(`Address ${address} has a low risk score: ${riskScore}`);
    }
};

const suspiciousAddresses = [
    "0x123456789abcdef",
    "0xabcdef123456789"
];

suspiciousAddresses.forEach(address => analyzeAddressRisk(address));

Would GoPlus be open to exploring such integrations in the future? If so, are there specific areas where external researchers and developers like myself could collaborate to support this effort?

Thank you for your dedication to blockchain security. I look forward to your response and hope to contribute to GoPlus's mission.