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

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O’Connor highlighted that what sets their system apart is “its emphasis on context and pattern recognition.” Isogawa explained that “AI can detect patterns often beyond the reach of human analysis.”Related: Jameson Lopp sounds alarm on Bitcoin address poisoning attacksThe machine learning approachO’Connor said Trugard generated synthetic training data for the AI to simulate various attack patterns.

Web2 security in a Web3 worldTrugard chief technology officer Jeremiah O’Connor told Cointelegraph that the team brings deep cybersecurity expertise from the Web2 world, which they’ve been “applying to Web3 data since the early days of crypto.” The team is applying its experience with algorithmic feature engineering from traditional systems to Web3.

These methods often fall behind evolving attacker tactics, techniques, and procedures.“The newly developed system instead leverages machine learning to create a system that learns and adapts to address poisoning attacks.

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Article Details

Author / Journalist: Cointelegraph by Adrian Zmudzinski

Category: Crypto

Markets: United States

Topics: Data System O’connor Model Crypto Address Webacy Poisoning Machine Learning Trugard

Source Website Secure: Yes (HTTPS)

News Sentiment: Neutral

Fact Checked: Legitimate

Article Type: News Report

Published On: 2025-05-21 @ 15:00:55 (15 hours ago)

News Timezone: GMT -5:00

News Source URL: cointelegraph.com

Language: English

Article Length: 538 words

Reading Time: 3 minutes read

Sentences: 21 lines

Sentence Length: 26 words per sentence (average)

Platforms: Desktop Web, Mobile Web, iOS App, Android App

Copyright Owner: © Coin Telegraph

News ID: 28890553

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About Coin Telegraph

Main Topics: Crypto

Official Website: cointelegraph.com

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News Last Updated: 7 hours ago

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