## 1 Introduction  
As more people and businesses conduct various personal and commercial activities online, the risk of becoming a victim of cybercrime and, in particular, fraud is very high. According to recent surveys [1; 2], there are several types of financial fraud, including but not limited to credit card fraud, insurance fraud, money laundering, fraudulent financial transactions, financial statement fraud, cryptocurrency fraud, healthcare fraud, and securities and commodities fraud. There are many research studies related to the problem of fraud detection as reviewed in refs. [1; 2]. Fraud can be implemented using a variety of tactics, including phishing. Phishing is a network attack that combines social engineering and computer technology to steal sensitive personal information from users [3]. Usually, attackers create phishing websites that look like legitimate ones to trick people into clicking links to malicious websites and submitting sensitive information, for example, account credentials. There are many studies that propose methods to detect phishing websites, as surveyed in refs. [3; 4]. However, few studies have explored the problem of fake online shops. Fraudulent online shops are the case of phishing websites that pretend to be legitimate. Many people in the world use online shops every day. According to Statista [5] about 2.14 billion people shopped online in 2021 and this number is growing constantly. It presents a good opportunity for attackers to exploit the naiveness and recklessness of buyers, offering fake online shops to obtain personal data, credit card data, or money from buyers. Scammers create stores that closely resemble real or existing stores of well-known brands. In this way, the buyer is made to believe that he or she is shopping at a legitimate and reliable store and, without hesitation, provides his or her personal data, credit card data, and pays for the goods. The buyer realizes the fraud when he or she does not receive the goods or sees that money has disappeared from his or her credit card account. According to Statista [6], 75% of consumers who were victims of online purchase scams worldwide lost money in 2021, while the median monetary loss per online purchase scam reached 101 US dollars [7]. Detecting scam online shops is typically based on website content and URL (Uniform Resource Locator), blacklists or whitelists, consumer reviews and complaints. Most researchers propose methods to detect scam websites using publicly available general data sets, which include phishing websites, fake news, piracy websites, etc. The most popular datasets used for the detection of phishing websites using machine learning techniques are PhishTank [8], Alexa [9], UCI Machine Learning Repository [10], OpenPhish [11], Common Crawl [12], and ISCXURL-2016 [13]. The machine learning approach is most common for the analysis of website content and address. Content analysis is based on the features related to HTML (Hyper Text Markup Language) [14; 15; 16], body text [17], images [18; 19; 20], domain registration information [15; 21] and styles [22]. The URL analysis [21; 23; 24; 25; 26; 27] is usually based on the features related to the length of the full URL, the domain name, the directory, the types of symbols, the protocol, the number of symbols, etc. Another popular way to assess whether a website is malicious is through various specialized portals that collect data on various fraudulent websites reported by customers. Popular platforms for collecting malicious websites, such as PhishTank [8], Alexa [9], ScamAdviser [28], URLVoid [29] or VirusTotal [30], have accumulated millions of entries in their blacklists, which allows users to judge whether a website is legitimate. Furthermore, various rating and review platforms, such as TrustPilot [31] can be used to assess the trustworthiness of websites, indicating a potentially malicious website if it has a lot of negative feedback.