gholamreza Ahmadi

Phishing is a cybercrime that involves the use of fake emails, messages, and websites to steal sensitive information such as passwords, credit card details, and other personal data. With the growth of the Internet and online transactions, phishing attacks have become increasingly sophisticated and difficult for individuals to detect and avoid. Therefore, researchers have made great efforts to identify and counter such attacks. Detecting phishing websites is one of the important challenges in cybersecurity (Khonji et al., 2014). Recent research has shown that machine learning methods can be effective in detecting such attacks (Ali, 2017; Dutta, 2021). Among the different approaches, deep learning has shown promising results due to its ability to automatically extract features (Yi et al., 2018). The aim of this research is to investigate methods for detecting phishing websites using machine learning algorithms. Machine learning can be a powerful tool in detecting phishing websites. By training machine learning algorithms on a large dataset of legitimate and fraudulent websites, the algorithms can learn to distinguish between the two. Using machine learning, it is possible to design automated systems for phishing detection that have high accuracy. Traditional phishing detection methods have relied mainly on static feature analysis (Jain & Gupta, 2017). However, more recent studies have shown that combining different machine learning methods can improve detection accuracy (Bhavani et al., 2022). In particular, the use of hybrid architectures such as CNN-LSTM has shown significant results in identifying malicious websites. (Alshingiti et al., 2023).