Smart contracts are the heart of the entire blockchain industry, from meme coins to complex DeFi platforms. These automated programs, however, face the persistent threat of cyberattacks, which often lead to significant financial and reputational losses. The best defense, according to a team of researchers, is artificial intelligence.

“Lightning Cat” is a novel solution that employs deep learning techniques to identify vulnerabilities​​ in smart contracts, proposed in a recent study titled, “Deep learning-based solution for smart contract vulnerabilities detection.”

Unlike traditional analysis tools—which are prone to both false positives and negatives—Lightning Cat utilizes deep learning methods to flag possible problems. It’s as if they trained a bot on the Solidity programming language instead of English.

“The results show that the proposed method has more reasonable data preprocessing and model optimization, resulting in better detection performance,” the researchers said, explaining that Lightning Cat is based on three optimized deep learning models: CodeBERT, LSTM, and CNN. These models undergo training on data sets comprising thousands of vulnerable contracts.

Notably, the CodeBERT model outperforms static detection tools, demonstrating an impressive f1-score of 93.53%, accurately capturing the syntax and semantics of the code​​ and proving

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Author: Jose Antonio Lanz

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