At approximately 20:05 UTC on 29 October, five Binance wallets on the XRP Ledger sent 233 million XRP to a single attacker-controlled address. Over the next 30 minutes, Binance hot wallets on Solana, NEAR, Base, Optimism, Bitcoin, Tron, Ethereum, Arbitrum and Avalanche were also drained. A second wave of withdrawals hit Binance wallets on Avalanche and Polygon shortly after 23:00 UTC.
The largest losses were on Ethereum, where $3.80 billion was taken across 186 different tokens, including 1.9 billion USDT and 823 million USDC. The attacker also stole 18,270 BTC, worth $1.48 billion, and 612.7 million USDT on Tron. Binance has suspended deposits and withdrawals, and has said that it will cover all losses using its SAFU fund and company funds.
Couldn't all of this been filtered for in real time ?
Probability distributions, ML classifiers, graph theoretic algorithms, etc. all seem to be useful for detecting these types of events in real time ?
Not being familiar with the crypto networks algorithms in detail enough to know, I'm spitballing this here.
The largest losses were on Ethereum, where $3.80 billion was taken across 186 different tokens, including 1.9 billion USDT and 823 million USDC. The attacker also stole 18,270 BTC, worth $1.48 billion, and 612.7 million USDT on Tron. Binance has suspended deposits and withdrawals, and has said that it will cover all losses using its SAFU fund and company funds.
Couldn't all of this been filtered for in real time ?
Probability distributions, ML classifiers, graph theoretic algorithms, etc. all seem to be useful for detecting these types of events in real time ?
Not being familiar with the crypto networks algorithms in detail enough to know, I'm spitballing this here.