Ecosystem/misc-research
1
0
Fork 0
mirror of https://github.com/Rucknium/misc-research.git synced 2024-12-22 11:29:22 +00:00
Code Issues Projects Releases Packages Wiki Activity Actions

Add spam end date to Monero-Black-Marble-Flood

Browse source
This commit is contained in:
Rucknium 2024-04-01 18:33:30 +00:00
parent 571ad0bd1f
commit 6c13a7f04f
4 changed files with 27 additions and 9 deletions
Show stats Download patch file Download diff file Expand all files Collapse all files

13
Monero-Black-Marble-Flood/code/effective-ring-size-projections.R
View file

@ -2,7 +2,7 @@
mean.coinbase.tx.size <- output.index[!duplicated(tx_hash) & tx_num == 1, mean(tx_size_bytes)]
num.blocks.since.spam <- output.index[block_height >= start.spam.height, uniqueN(block_height)]
num.blocks.during.spam <- output.index[block_height %between% c(start.spam.height, end.spam.height), uniqueN(block_height)]
mean.size.1in.2out <- spam.results[[1]]$non.spam.fingerprint.tx[number_of_inputs == 1 & number_of_outputs == 2, mean(tx_size_bytes)]
mean.size.2in.2out <- spam.results[[1]]$non.spam.fingerprint.tx[number_of_inputs == 2 & number_of_outputs == 2, mean(tx_size_bytes)]
@ -26,10 +26,17 @@ sim.spam <- lapply(c(11, 16, 25, 40, 60), FUN = function(ring.size.sim) {
tx_weight_bytes - mean.size.16.ring.input * number_of_inputs + number_of_inputs * mean.size.one.ring.member * ring.size.sim]
mean.non.spam.kb.per.block <- mean.coinbase.tx.size/1000 +
(sum(non.spam.fingerprint.tx.sim[block_height >= start.spam.height, tx_weight_bytes.sim])/num.blocks.since.spam)/1000
(sum(non.spam.fingerprint.tx.sim[block_height %between%
c(start.spam.height, end.spam.height), tx_weight_bytes.sim])/num.blocks.since.spam)/1000
mean.non.spam.output.per.block <- nrow(non.spam.fingerprint[block_height %between%
c(start.spam.height, end.spam.height), ])/num.blocks.since.spam
mean.non.spam.output.per.block <- nrow(non.spam.fingerprint[block_height >= start.spam.height, ])/num.blocks.since.spam
# Don't do this because the number of blocks after start.spam.height in
# non.spam.fingerprint.tx.sim is a little smaller than output.index:
# mean.non.spam.kb.per.block <- mean.coinbase.tx.size/1000 +
# (sum(non.spam.fingerprint.tx.sim[block_height >= start.spam.height, tx_weight_bytes.sim/uniqueN(block_height)]))/1000
# mean.non.spam.output.per.block <- non.spam.fingerprint[block_height >= start.spam.height, .N/uniqueN(block_height)]
mean.effective.ring.size <- ring.size.sim
simulated.adversary.owned.outputs <- 0

2
Monero-Black-Marble-Flood/code/fee-behavior.R
View file

@ -28,6 +28,7 @@ ggplot(all.tx.volume.fees, aes(x = block_date,
colour = factor(fee_per_byte_nanoneros.cut))) +
geom_line(linewidth = 1.5) +
geom_vline(xintercept = start.spam.date, linetype = 2) +
geom_vline(xintercept = end.spam.date, linetype = 2) +
scale_y_continuous( limits = c(0, 1), expand = c(0, 0), labels = scales::label_percent()) +
scale_x_date(breaks = "3 day", expand = c(0, 0), guide = guide_axis(angle = 90)) +
ggtitle("Share of transactions by fee tier (all transactions)") +
@ -71,6 +72,7 @@ ggplot(non.spam.fingerprint.tx.fees, aes(x = block_date,
colour = factor(fee_per_byte_nanoneros.cut))) +
geom_line(linewidth = 1.5) +
geom_vline(xintercept = start.spam.date, linetype = 2) +
geom_vline(xintercept = end.spam.date, linetype = 2) +
scale_y_continuous( limits = c(0, 1), expand = c(0, 0), labels = scales::label_percent()) +
scale_x_date(breaks = "3 day", expand = c(0, 0), guide = guide_axis(angle = 90)) +
ggtitle("Share of transactions by fee tier (suspected spam removed)") +

2
Monero-Black-Marble-Flood/code/mempool-tx-confirmation.R
View file

@ -15,6 +15,7 @@ png("mean-delay-first-confirmation.png", width = 500, height = 600)
ggplot(mempool.hourly[block_receive_time.hour >= as.POSIXct(start.spam.date - 3),], aes(x = block_receive_time.hour, y = confirmation.latency/60)) +
geom_line() +
geom_vline(xintercept = mempool[block_height == start.spam.height, block_receive_time_UTC[1]], linetype = 2) +
geom_vline(xintercept = mempool[block_height == end.spam.height, block_receive_time_UTC[1]], linetype = 2) +
scale_y_continuous(breaks = seq(0, 600, by = 30), limits = c(0, NA), expand = c(0, 0)) +
scale_x_datetime(breaks = "day", expand = c(0, 0), guide = guide_axis(angle = 90)) +
ggtitle("Mean delay to first transaction confirmation") +
@ -57,6 +58,7 @@ png("max-delay-first-confirmation.png", width = 500, height = 600)
ggplot(hourly.max.confirmation.latency[hour.bin >= as.POSIXct(start.spam.date - 3),], aes(x = hour.bin, y = confirmation.latency/60^2)) +
geom_line() +
geom_vline(xintercept = mempool[block_height == start.spam.height, block_receive_time_UTC[1]], linetype = 2) +
geom_vline(xintercept = mempool[block_height == end.spam.height, block_receive_time_UTC[1]], linetype = 2) +
scale_y_continuous(breaks = seq(0, 24*5, by = 3),limits = c(0, NA), expand = c(0, 0)) +
scale_x_datetime(breaks = "day", expand = c(0, 0), guide = guide_axis(angle = 90)) +
ggtitle("Maximum delay to first transaction confirmation") +

19
Monero-Black-Marble-Flood/code/spam-assumptions.R
View file

@ -3,6 +3,12 @@
start.spam.height <- 3097764 # 2024-03-04 15:21:24
start.spam.date <- as.Date("2024-03-04")
end.spam.height <- 3114046 # 2024-03-27 06:30:37 UTC
end.spam.date <- as.Date("2024-03-27")
library(ggplot2)
output.index[, block_date.week.day := weekdays(block_date)]
@ -41,7 +47,7 @@ for (spam.type in seq_along(spam.types)) {
by = "block_date.week.day"]
spam.fingerprint <- output.index[
block_height >= start.spam.height &
block_height %between% c(start.spam.height, end.spam.height) &
tx_num != 1 &
eval(spam.types[[spam.type.sub]]$fingerprint.crieria), ]
@ -70,8 +76,8 @@ for (spam.type in seq_along(spam.types)) {
non.spam.fingerprint <- output.index[ tx_num != 1 &
(
block_height < start.spam.height |
(block_height >= start.spam.height &
(! block_height %between% c(start.spam.height, end.spam.height)) |
(block_height %between% c(start.spam.height, end.spam.height) &
! (tx_hash %chin% spam.fingerprint.tx$tx_hash))
), ]
@ -182,7 +188,7 @@ all.output.volume.by.day <- all.output.volume.by.day[-.N, ]
png("spam-share-outputs.png", width = 600, height = 600)
ggplot(all.output.volume.by.day[block_date >= start.spam.date, ], aes(x = as.POSIXct(block_date), y = spam.share.outputs)) +
ggplot(all.output.volume.by.day[block_date %between% c(start.spam.date, end.spam.date), ], aes(x = as.POSIXct(block_date), y = spam.share.outputs)) +
geom_line() +
scale_y_continuous(limit = c(0, 1), expand = c(0, 0), labels = scales::label_percent()) +
scale_x_datetime(date_breaks = "day", guide = guide_axis(angle = 90)) +
@ -200,8 +206,9 @@ dev.off()
mean.spam.share.outputs <- all.output.volume.by.day[block_date >= (start.spam.date + 1), mean(spam.share.outputs)]
# Skip the first day because suspected spam started in the middle of the day
mean.spam.share.outputs <- all.output.volume.by.day[block_date %between%
c(start.spam.date + 1, end.spam.date - 1), mean(spam.share.outputs)]
# Skip the first and last days because suspected spam started in the middle of the days
binom.ring.size <- rbind(
data.table(x = 1:16, y = dbinom(0:15, size = 11, prob = 1 - 192/233),