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Explore a groundbreaking research presentation that investigates front-running bot evasion strategies in blockchain ecosystems. Delve into the critical security challenges facing Ethereum and BNB Smart Chain, where front-running attacks have resulted in multi-billion dollar losses through automated bots that exploit transaction ordering. Learn about EVScope, an innovative framework that combines binary analysis and machine learning techniques to detect both known and previously unknown evasion strategies employed by blockchain developers. Examine findings from an extensive analysis of 6,761,186 arbitrage transactions and 71 significant attack transactions, revealing 32 refined evasion strategies across four key categories: access control, profit control, execution split, and code obfuscation. Discover how 25 of these strategies are newly introduced research contributions, with 28 being first applied specifically to front-running evasion, addressing a significant gap in blockchain security literature. Gain insights into the covert nature of these defensive tactics and their implications for strengthening blockchain application layer resilience against automated front-running threats.
