eth-phishing-detect VS ranklens

The project is called MATRIX ABYSS, but their website is https://abyssmatrix.world. When I looked up https://matrixabyss.world/ there is an error saying that this website has been flagged for phishing, by metamask eth-phishing-detect. Super sus.

This was an issue with Metamask's fuzzy filtering, see here. The issue has been resolved, and it's been plenty of time for the change to propagate around. Not sure if you need to update your Metamask or clear your cache or something. Unfortunate that it's still causing trouble.

MetaMask already has a blacklist for phishing websites: https://github.com/MetaMask/eth-phishing-detect

Yea, no idea why: https://github.com/MetaMask/eth-phishing-detect/issues/7876

they added sudoswap to their fuzzy list https://github.com/MetaMask/eth-phishing-detect/pull/7633 and that caused issues with other domains with "swap" in the name

According to the code on https://github.com/MetaMask/eth-phishing-detect/blob/45ea5cf..., looks like that everything within Levenstein distance of 3 from whitelisted hosts (like "metamask.*") is blocked.

Metarank and Metamask have the distance of 3. I've made a ticket some time ago in their github repo (https://github.com/MetaMask/eth-phishing-detect/issues/6855), but it seems that it was lost in thousands of similar tickets.

Do you have an old version of Metamask? That site was accidentally picked up by a blacklist regex but was whitelisted again back in october - https://github.com/MetaMask/eth-phishing-detect/pull/5563

Right now it runs in a dev-mode on a single EC2 t3.large instance with loadavg ~0.30, but the inference load is quite tiny right now: around 3-4 reranking requests per second. And yes, as a typical open-source project it still crashes from time to time :)

The training dataset is not that huge (see https://github.com/metarank/ranklens/ for details, it's open-source), so we do a full retraining directly on the node right after the deployment, and it takes around 1 minute to finish. We also run the same process inside the CI: https://github.com/metarank/metarank/blob/master/run_e2e.sh

There is an option to run this thing in a distributed mode:

* training is done using a separate batch job running on Apache Flink (and on k8s using flink's integration)

* feature updates are done in a separate streaming Flink job, writing everything in Redis

* The API fetches latest feature values from Redis and runs the ML model.

The dev-mode I've mentioned earlier is when all these three things are bundled together in a single process to make it easier to play with the tool. But we didn't spent much time testing distributed setup, as this thing is still a hobby side-project and we're limited in time spent developing it.