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Sybil Shield

About

SybilShield is an open-source project built for the StarkNet ecosystem that utilizes blockchain data to detect Sybil attack behaviors.

A Sybil attack is a type of attack where an attacker creates multiple fake identities (also known as Sybil nodes) in a network in order to gain control or disrupt the network's operation.

SybilShield is implemented using Apibara technology to explore on-chain data, allowing it to detect and mitigate Sybil attacks by identifying and isolating fake identities.

Hacker House

We started from scratch, and we expected to have the following points:

  • A front (hosted locally or on a server) which displays the node interactions around the targeted contract. The front-end technology will be in typescript react and we can use viz.js library for rendering or equivalent.
  • A back (hosted locally or on a server) which fetches the starknet data (using apibara or equivalent), runs a model, and returns the result to the front using HTTP routes. The back-end technoology could be typescript or python according to available skills in the team.
  • At least 1 model that provides a guilty score for each nodes

Implementation

The team was composed of the following skills:

  • 2 back-end developers Bal7hazar & trangnv
  • 1 UX/UI designer G2CARBZ
  • 1 front-end developer schwepps
  • 1 data scientist marissaposner

So far, we have:

  • Implemented the front-end as expected and deployed it on a server (paid by Carbonable).
  • Setup the backend, create routes, model handler, main controller and hosted the back on a server (paid by Carbonable).
  • Benchmarked on different apibara alternatives to workaround data gathering issues (checkpoint, starkscan).
  • Benchmarked on sybil attacks models and plug it in our model handler (this latest point is still on going)

Issues we are facing:

  • Gathering data takes too much time
  • Processing data takes too much time
  • Define relevant models takes much more time, since we need to iterate over model implementations
  • Traversing a large graph network is computationally expensive

Front pull requests

Back-end pull requests

Checkpoint alternative exploration

After the event

We need to explore more alternatives to workaround about our issues, what we have in mind is:

  • use a blockchain sync database to improve data accessiblity and data processing
  • pay for a Starkscan api key to use their api and fetch data from them
  • continue to test and improve models to detect suspicious addresses

Final goal is to have a efficient way for a user to analyze relevantly any input contract