The Quest Seasons and Leagues Sybil Detection

General
1

Hi hope everyone is doing well.

It has been brought to my attention that some of the top ten accounts in the current league are suspicious.

We are only in season 1 of the current quests, and there are already some suspicious activities appearing at the first opportunity.

It’s not fair that this is happening, so I’ve asked the A.I. to help me draft a proposal. I’ll share it with you all for discussion, and we can consider ways to improve it or make necessary changes before finalizing it.

[DCP-#] Proposal for Funding and Hiring a Team to Detect Sybil Accounts in DappRadar Quest Seasons and Future DAO League

Summary

This proposal seeks to secure funding for and the hiring of a specialized team dedicated to detecting and preventing Sybil accounts within the DappRadar Quest Seasons and the upcoming DAO League. The objective is to maintain the integrity of our platforms by ensuring fair play and genuine participation.

Abstract

Sybil attacks, where a single entity creates multiple accounts to gain unfair advantages, pose significant threats to the integrity of the DappRadar Quest Seasons and the forthcoming DAO League. To combat this, we propose the establishment of a dedicated team responsible for identifying and mitigating such fraudulent activities. This team will utilize advanced detection methods, including behavioral analytics, machine learning, and blockchain analysis, to ensure a fair and secure environment for all participants.

Motivation

The presence of Sybil accounts undermines the trust and fairness of the DappRadar platform. These fraudulent activities not only distort competition but also deter genuine users from participating. By addressing this issue proactively, we aim to enhance the credibility and attractiveness of our Quest Seasons and DAO League, thereby fostering a more engaged and committed community.

Specification

  1. Team Composition and Roles:
  • Team Lead: Oversees the team’s operations, sets goals, and ensures alignment with DappRadar’s objectives.
  • Data Analysts: Experts in behavioral analytics and machine learning to identify suspicious activity patterns.
  • Blockchain Analysts: Specialize in on-chain analysis to track and validate account authenticity.
  • Security Specialists: Focus on developing and implementing security protocols to prevent Sybil attacks.
  • Community Managers: Engage with the community to educate and report suspicious activities.
  1. Detection Methods:
  • Behavioral Analytics: Analyzing user behavior to identify patterns indicative of Sybil activities.
  • Machine Learning Models: Developing models that can predict and flag potential Sybil accounts based on historical data.
  • On-Chain Analysis: Monitoring blockchain transactions to identify anomalies in account creation and activity.
  • Manual Verification: Conducting random manual checks on flagged accounts to ensure accuracy.
  1. Tools and Technologies:
  • AI and Machine Learning Platforms: Utilize tools like TensorFlow or PyTorch for developing detection algorithms.
  • Blockchain Analytics Platforms: Leverage platforms like Chainalysis or Nansen for on-chain monitoring.
  • Data Visualization Tools: Use tools like Tableau or Power BI for visualizing suspicious activity patterns.
  1. Implementation Phases:
  • Phase 1: Planning and Team Formation: Define the scope, hire the team, and set up necessary infrastructure.
  • Phase 2: Development and Testing: Develop detection algorithms, conduct initial tests, and refine based on feedback.
  • Phase 3: Deployment and Monitoring: Implement detection systems in live environments, continuously monitor and adjust.
  • Phase 4: Community Engagement: Educate the community on Sybil attack prevention, encourage reporting of suspicious activities.
  1. Budget:
  • Salaries: Competitive salaries for a team of 5-7 members, including benefits.
  • Technology and Tools: Investment in software, hardware, and analytics tools.
  • Operational Costs: Office space, administrative expenses, and training programs.
  • Contingency Fund: Reserve for unexpected expenses and rapid response to emerging threats.

Benefits

  • Enhanced Integrity: Ensures fair competition and trust within the DappRadar community.
  • Increased Participation: Attracts more genuine users by providing a secure and trustworthy platform.
  • Reputation Boost: Strengthens DappRadar’s reputation as a leading platform in maintaining fairness and security.
  • Long-Term Sustainability: Prevents fraudulent activities that could harm the platform’s long-term success.

Drawbacks

  • Initial Costs: Significant upfront investment in hiring and setting up the team.
  • False Positives: Potential for legitimate accounts to be incorrectly flagged, requiring a robust appeals process.
  • Ongoing Maintenance: Continuous effort and resources required to adapt to evolving Sybil attack methods.

Vote

  • For: Approve the proposal to fund and hire a dedicated team for detecting and preventing Sybil accounts in DappRadar Quest Seasons and the DAO League.
  • Against: Reject the proposal and continue without a specialized team, relying on existing methods for Sybil detection.

By discussing and utilizing this proposal, we take a significant step towards safeguarding the integrity of our platforms, ensuring a fair and engaging environment for all participants.

Let’s ensure we have measures in place to safeguard our community. I suspect that the same group causing issues in our league with Sybil accounts might also be responsible for the scam bots appearing in our Discord.

Thanks for taking the time to read. (i will be making this proposal soon if nobody has any other input)

On another note, if we agree, it would be great to have this implemented by season 2 or as soon as possible.

2

Thank you for bringing this to our attention, Ash. As seen in our previous PRO Airdrops, the team is highly committed to ensuring that legitimate community members can reap the rewards of their commitment. In the previous strategy, we outlined that this will always be a cat-and-mouse game between the projects looking to reward users and bad actors looking to exploit these rewards. It’s necessary to balance our policy of rewards in a transparent way that doesn’t jeopardize the integrity of the process.

We have seen much success with this in the LayerZero airdrop, where they used a combination of data analytics and community support to create the guidelines for their airdrop. It was not perfect, but it ensured sufficient community contributions and more eyes to determine whether or not a wallet was eligible. With Season 1 of gamification coming to an end soon, this will be even more important.

We have approximately $3,000,000 worth of RADAR in our community pool currently assigned for these types of rewards, and with this amount of money, it’s clear that quite a few bad actors will appear. The creation of an anti-sybil group this large initially seems excessive when considering that the distribution of tokens does not happen on a regular basis.

I believe that we could implement a similar campaign to what LayerZero did, where we give the community a chance to explain whether they are a sybil or not, and then also give the community a chance to identify sybils and get rewarded for it too. This could have a community lead from the team to help coordinate this process, which would be more effective given our current scale of token distributions.

Additionally, if we’re able to utilize our internal skills and identify unique active wallets, we could provide this as a service to other communities as well for revenue streams and in order to minimize these risks moving forward.

1 Like
3

It might seem small, but as we grow, I think it’s going to be needed. With the community doing the reporting, we have many involved (one person informed me about it, and three others, including me and Mni, mentioned multiple accounts right at the start). It could end up being bots reporting bots and getting paid for it, which sounds like a bad idea.

The team can start as small as one person or possibly three. I would like to be on this team and part of the process of catching the bots. As real people start to join because the rewards are worth it, the team will need to grow to ensure they aren’t being overtaken by a bot farm.

Community-Led Coordination:

  • I suggest having a community lead from the team to coordinate the process of identifying Sybil accounts, leveraging community insights and efforts. This could be either the DappRadar team (who has not yet addressed this) or the new Sybil detection team that will put a stop to these shenanigans. I’d like to Sybil check the earn-to-grass program as well.

Anything involving RADAR rewards, contribute, leagues, and related tokenomics should undergo Sybil checking. The DAO league could get a mini pass for some tasks, allowing referrals for DOD liquidity, token swaps via our DEX, or listing NFTs on our partners’ marketplace. This way, we can put these accounts to good use instead of letting them steal multiple spots in the rankings and prizes.

On top of this, any account involved in theft, phishing, or similar activities should be banned from the league, platform, Discord, and Telegram, unless they were the victim. We should be able to detect and identify true victims of attacks.

4

Thank you for your response, nathan While the idea of a community-driven approach to Sybil detection is valuable, I believe the complexity and potential impact on our rewards system necessitate a more robust and dedicated solution. Here’s a more detailed proposal on why a specialized team is crucial:

The Need for a Dedicated Sybil Detection Team

  1. Scale and Complexity:
  • Our community is vast, and community reporting alone might not be sufficient. With only a few individuals reporting, we risk underreporting or misidentification.
  1. Risk of Exploitation:
  • Allowing community members to report Sybil accounts for rewards could lead to bots reporting other bots, further exploiting the system.

Proposed Solution

  1. Formation of a Sybil Detection Team:
  • Start with a small team, potentially one to three members, including the person who identified the issue. They will develop and implement strategies to detect and eliminate Sybil accounts.
  1. Gradual Scaling:
  • Expand the team as needed to handle the growing workload and maintain the integrity of our rewards system.
  1. Training and Expertise:
  • Train team members in advanced detection techniques, ensuring they have the necessary tools to identify Sybil accounts effectively.
  1. Integration with Current Programs:
  • The team will focus on all reward-related programs, including the DAO league and earn-to-grass program.
  1. KYC for Top Winners:
  • Implement KYC checks for the winners of each season to ensure only legitimate participants claim rewards.

Enhancing Community Coordination

  1. Community Lead:
  • Appoint a community lead from the Sybil detection team to coordinate efforts and serve as a liaison between the community and the detection team.
  1. Sybil Checking Flexibility:
  • Allow some tasks within the DAO league to receive a Sybil check pass for legitimate activities, maintaining overall integrity.

Comprehensive Ban on Malicious Accounts

  1. Immediate Bans:
  • Implement a policy to immediately ban any accounts involved in theft, phishing, or other malicious activities from the league, platform, and community channels (Discord, Telegram, etc.). Victims of attacks will be identified and protected, ensuring fairness and security.

Conclusion

The implementation of a dedicated Sybil detection team is necessary to safeguard the integrity of our reward systems. By combining focused detection efforts with community participation and robust policies, we can create a fair and transparent environment for all participants.

I look forward to discussing this proposal further and working towards a solution that benefits our entire community.

5

Example of an account in the Leaderboard that might be suspicious.

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