rich1.0npub17fjkngg0s0mfx4uhhz6n4puhflwvrhn2h5c78vdr5xda4mvqx89swntr0shttps://nostr.ae/npub17fjkngg0s0mfx4uhhz6n4puhflwvrhn2h5c78vdr5xda4mvqx89swntr0snjumphttps://nostr.ae📅 Original date posted:2022-06-29 📝 Original message: During the recent Oakland Dev Summit, some lightning engineers got together to discuss DoS protection for onion messages. Rusty proposed a very simple rate-limiting scheme that statistically propagates back to the correct sender, which we describe in details below. You can also read this in gist format if that works better for you [1]. Nodes apply per-peer rate limits on _incoming_ onion messages that should be relayed (e.g. N/seconds with some burst tolerance). It is recommended to allow more onion messages from peers with whom you have channels, for example 10/seconds when you have a channel and 1/second when you don't. When relaying an onion message, nodes keep track of where it came from (by using the `node_id` of the peer who sent that message). Nodes only need the last such `node_id` per outgoing connection, which ensures the memory footprint is very small. Also, this data doesn't need to be persisted. Let's walk through an example to illustrate this mechanism: * Bob receives an onion message from Alice that should be relayed to Carol * After relaying that message, Bob stores Alice's `node_id` in its per-connection state with Carol * Bob receives an onion message from Eve that should be relayed to Carol * After relaying that message, Bob replaces Alice's `node_id` with Eve's `node_id` in its per-connection state with Carol * Bob receives an onion message from Alice that should be relayed to Dave * After relaying that message, Bob stores Alice's `node_id` in its per-connection state with Dave * ... We introduce a new message that will be sent when dropping an incoming onion message because it reached rate limits: 1. type: 515 (`onion_message_drop`) 2. data: * [`rate_limited`:`u8`] * [`shared_secret_hash`:`32*byte`] Whenever an incoming onion message reaches the rate limit, the receiver sends `onion_message_drop` to the sender. The sender looks at its per-connection state to find where the message was coming from and relays `onion_message_drop` to the last sender, halving their rate limits with that peer. If the sender doesn't overflow the rate limit again, the receiver should double the rate limit after 30 seconds, until it reaches the default rate limit again. The flow will look like: Alice Bob Carol | | | | onion_message | | |------------------------>| | | | onion_message | | |------------------------>| | | onion_message_drop | | |<------------------------| | onion_message_drop | | |<------------------------| | The `shared_secret_hash` field contains a BIP 340 tagged hash of the Sphinx shared secret of the rate limiting peer (in the example above, Carol): * `shared_secret_hash = SHA256(SHA256("onion_message_drop") || SHA256("onion_message_drop") || sphinx_shared_secret)` This value is known by the node that created the onion message: if `onion_message_drop` propagates all the way back to them, it lets them know which part of the route is congested, allowing them to retry through a different path. Whenever there is some latency between nodes and many onion messages, `onion_message_drop` may be relayed to the incorrect incoming peer (since we only store the `node_id` of the _last_ incoming peer in our outgoing connection state). The following example highlights this: Eve Bob Carol | onion_message | | |------------------------>| onion_message | | onion_message |------------------------>| |------------------------>| onion_message | | onion_message |------------------------>| |------------------------>| onion_message | |------------------------>| Alice | onion_message_drop | | onion_message | +----| |------------------------>| onion_message | | | |--------------------|--->| | | | | | | | | | | | | | onion_message_drop |<-------------------+ | |<------------------------| | In this example, Eve is spamming but `onion_message_drop` is propagated back to Alice instead. However, this scheme will _statistically_ penalize the right incoming peer (with a probability depending on the volume of onion messages that the spamming peer is generating compared to the volume of legitimate onion messages). It is an interesting research problem to find formulas for those probabilities to evaluate how efficient this will be against various types of spam. We hope researchers on this list will be interested in looking into it and will come up with a good model to evaluate that scheme. To increase the accuracy of attributing `onion_message_drop`, more data could be stored in the future if it becomes necessary. We need more research to quantify how much accuracy would be gained by storing more data and making the protocol more complex. Cheers, Bastien [1] https://gist.github.com/t-bast/e37ee9249d9825e51d260335c94f0fcf -------------- next part -------------- An HTML attachment was scrubbed... URL: <http://lists.linuxfoundation.org/pipermail/lightning-dev/attachments/20220629/513b6829/attachment.html>