New York City Mayor Eric Adams has announced plans to form a digital advisory council that would guide the city&#8217;s digital asset strategy. Speaking at the New York City Crypto Summit on May 20, Adams said it has the potential…

New York City to establish crypto advisory council, says Mayor Eric Adams

U.S. stocks dipped in early trading on Wednesday, with the Dow Jones 
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jitters over trade tensions between China and the United States. With stocks 
paring some gains early in…

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Bitcoin is just 3% shy of a new all-time high, and the current market 
structure suggests a breakout is imminent. Key technical signals point to 
strong bullish momentum. Bitcoin (BTC) is on the verge of making history again 
as it…

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Blockchain tech firm BTCS has acquired 3,450 ether for $8.42 million, lifting 
its total ETH holdings to 12,500. The move reflects a broader strategy to 
scale infrastructure and staking operations. BTCS Adds 3,450 Ethereum in 
Strategic Expansion BTCS Inc. (Nasdaq: BTCS) has announced the purchase of 
3,450 ETH for $8.42 million, marking a 38% increase […]

BTCS Boosts Ethereum Holdings to 12,500 ETH With $8.42 Million Acquisition

Crypto cybersecurity firm Trugard and onchain trust protocol Webacy have 
developed an artificial intelligence-based system for detecting crypto wallet 
address poisoning.
According to a May 21 announcement shared with Cointelegraph, the new tool is 
part of Webacy&#8217;s crypto decisioning tools and &#8220;leverages a supervised machine 
learning model trained on live transaction data in conjunction with onchain 
analytics, feature engineering and behavioral context.&#8221;
The new tool purportedly has a success score of 97%, tested across known 
attack cases. &#8220;Address poisoning is one of the most underreported yet costly 
scams in crypto, and it preys on the simplest assumption: That what you see is 
what you get,&#8221; said Webacy co-founder Maika Isogawa.
_Address poisoning detection infographic. Source: Trugard and Webacy_
Crypto address poisoning is a scam where attackers send small amounts of 
cryptocurrency from a wallet address that closely resembles a target&#8217;s real 
address, often with the same starting and ending characters. The goal is to 
trick the user into accidentally copying and reusing the attacker&#8217;s address in 
future transactions, resulting in lost funds.
The technique exploits how users often rely on partial address matching or 
clipboard history when sending crypto. A January 2025 study found that over 
270 million poisoning attempts occurred on BNB Chain and Ethereum between July 
1, 2022, and June 30, 2024. Of those, 6,000 attempts were successful, leading 
to losses over $83 million.
_**Related:**__**What are address poisoning attacks in crypto and how to avoid 
them?**_
## Web2 security in a Web3 world
Trugard chief technology officer Jeremiah O&#8217;Connor told Cointelegraph that the 
team brings deep cybersecurity expertise from the Web2 world, which they&#8217;ve 
been &#8220;applying to Web3 data since the early days of crypto.&#8221; The team is 
applying its experience with algorithmic feature engineering from traditional 
systems to Web3. He added:
&gt; &#8220;Most existing Web3 attack detection systems rely on static rules or basic 
&gt; transaction filtering. These methods often fall behind evolving attacker 
&gt; tactics, techniques, and procedures.&#8221;
The newly developed system instead leverages machine learning to create a 
system that learns and adapts to address poisoning attacks. O&#8217;Connor 
highlighted that what sets their system apart is &#8220;its emphasis on context and 
pattern recognition.&#8221; Isogawa explained that &#8220;AI can detect patterns often 
beyond the reach of human analysis.&#8221;
_**Related:**__**Jameson Lopp sounds alarm on Bitcoin address poisoning 
attacks**_
## The machine learning approach
O&#8217;Connor said Trugard generated synthetic training data for the AI to simulate 
various attack patterns. Then the model was trained through supervised 
learning, a type of machine learning where a model is trained on labeled data, 
including input variables and the correct output.
In such a setup, the goal is for the model to learn the relationship between 
inputs and outputs to predict the correct output for new, unseen inputs. 
Common examples include spam detection, image classification and price 
prediction.
O&#8217;Connor said the model is also updated by training it on new data as new 
strategies emerge. &#8220;To top it off, we&#8217;ve built a synthetic data generation 
layer that lets us continuously test the model against simulated poisoning 
scenarios,&#8221; he said. &#8220;This has proven incredibly effective in helping the 
model generalize and stay robust over time.&#8221;
_**Magazine:**__**Crypto-Sec: Phishing scammer goes after Hedera users, 
address poisoner gets $70K**_

AI tool claims 97% efficacy in preventing ‘address poisoning’ attacks

Circle has officially launched the mainnet for its Circle Payments Network. 
The CPN will be a new blockchain-based system designed to facilitate real-time 
cross-border payments and settlements using its stablecoin, USDC (USDC). The 
CPN is built to support business-to-business payments,…