## 1 Introduction  
Currently, most companies and organizations provide fast and easy access to the services we use in daily life by digitizing them. However, this has brought the problem of data security. The personal information, financial information, and passwords used to access these services may lead to a data security problem. There are many different types of cyber attacks used to obtain personal and financial data. Phishing attacks, one of these types of attacks, is the stealing of users' personal and financial information through a fake website created by copying a legitimate and known website [1]. Phishing websites are designed by copying legal websites and creating similar ones. Hence, victims have no doubts about the website they entered, as there is little difference between a phishing website and the legitimate one. The Anti-Phishing Working Group (APWG), which is supported by many IT security companies, is an organization studying the effects of phishing attacks [2]. Although the motivation and attack strategies of the attackers are known, a fully effective method to prevent phishing attacks has not been developed yet. In the third quarter of 2022, the APWG observed 1,270,883 total phishing attacks, a new record, and the worst quarter for phishing that the APWG has ever observed so far [3]. In this time interval, the APWG's founding member, OpSec Security found that phishing attacks against the financial sector, which includes banks, remained the largest set of attacks, accounting for approximately 23% of all phishing. The attacks against webmail and software-as-a-service providers remained steady, at approximately 17% of all attacks, while the attacks against retail/e-commerce sites fell to 4%, down from 14% in the first quarter. Phishing against social media companiestrended downward to 11%, after fluctuating from 8% of all attacks in the fourth quarter of 2021 to 15% in the second quarter of 2022. Phishing against cryptocurrency targets fell from 4% in the second quarter to 2% in the third quarter [2; 3]. Furthermore, phishing websites have been spreading by advertising on social media platforms or sending messages to predetermined victims on social media [4]. As the threat rapidly increases, various strategies have been developed to detect these attacks. The most common of those strategies, blacklisting, involves creating a list of known malicious websites that are known to be associated with phishing attacks [1]. When a user attempts to access a website, the system can check the domain of the website against the blacklist and block access if it appears on the list. Blacklisting can be an effective way to block known phishing websites, but it is not foolproof. New phishing websites are constantly being created, and it can be difficult to keep the blacklist up to date. Additionally, attackers can sometimes use domain names or websites that are not on the blacklist, making it harder to detect and block their attacks. On the other hand, machine learning involves using algorithms that can learn from and make predictions or decisions based on data [5; 6]. In the context of phishing attack detection, a machine learning model could be trained on a dataset of known phishing and legitimate websites. The model would then be able to learn the characteristics that are commonly associated with phishing attacks and use this knowledge to make predictions about the likelihood that a new website is a phishing attack. Machine learning-based approaches to phishing attack detection can be more effective than blacklisting, as they can adapt to new types of phishing attacks and do not require manual updates to a blacklist. However, selected features and classification algorithms have a direct influence on attack detection performance, such that a poor choice of features and classifiers may degrade the performance significantly.