The inspiration is the problem statement shared by Prof. Clifford Neumann shared here.

What it does

Zeta means “Born Last” and therefore, this score would tell you until the last seen transaction on the chain, in this case, Ethereum, the chances of a node being malicious and based on a very simple trust mechanism. This score allows all accounts on the chain to get a zeta score which indicates the maliciousness level of that node.

We made the chain as a Graph, where all the nodes are the accounts and all the edges are the transactions.

After scrapping all the Ethereum accounts from different online sources with a 100% confidence, we use those accounts as malicious nodes and we assign zeta score of 0 to those accounts and then start assigning zeta score to all the other accounts on the closeness on the node to the malicious nodes.

How I built it

We scrapped the internet looking for famous Ransomeware cases and extracting those addresses and then create a graph for the Ethereum network

Challenges I ran into

The challenges:

  1. Extracting addresses from random websites lead to different kinds of challenges like trying to scroll and scrape
  2. Couldn't find enough sources for Ethereum addressed that have been received funding for ransomware
  3. The web3 interfacing leads to a lot of challenges

Accomplishments that I'm proud of

Zeta and Algorithmic capabilities in Javascript

What I learned

React integration with Web3 Chacha, Yoga

What's next for Zeta

Zeta has the capability to be distributed across the blockchain as we put the zeta changes on the Ethereum chain and any user doing transactions with an account on the Ethereum blockchain would be able to understand the maliciousness level for all accounts it wants to transact with and avoid being involved in a transaction with a malicious node.

Share this project: