The decentralized digital era demands applications that keep your wallet credentials safe. As more people embrace digital currencies and online financial transactions, the need for robust security measures becomes increasingly important. Transactions on a blockchain network are relatively safe because of their decentralized nature. Still, the website in which the credentials are entered becomes a point of vulnerability or weakness to Phishing attacks targeting wallet applications and smart contracts, which can be particularly harmful, as they may result in loss of funds or compromising sensitive information. These attacks aim to trick users into revealing sensitive information, such as usernames, passwords, or private keys, by impersonating a trustworthy entity. This study presents a system for identifying phishing URLs, by utilizing a type of 1D ResNet that learns incrementally, combined with a continuously updating blacklists system to stay current with the evolving trends in malicious URL configurations. The proposed scheme AdaptPhishSysNet is compared with various machine learning models and other URL phishing detection methods, and the results show that the scheme can classify the phishing attacks in the long run without a high toll on the model’s ability to generalize.