## 4 Materials and Methods
### Data Preparation
\begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Feature** & **Description** & **Datatype** \\ \hline Presence of redirects & Presence of forced off-domain redirects. A value of 1 is assigned if present, 0 otherwise. & Binary \\ \hline Site reputation & Site reputation as determined by Scam Adviser [92], a sensor that gauges the site’s quality based on evaluations of pirated content, phishing, or fraud on a scale from 0 to 100. A value of 1 is added if the reputation is below 60; otherwise, 1 is added. & Binary \\ \hline Adware & A value of 1 is added if any of the links contained on the site are detected by VirusTotal [93] with a detection rate of over 60\% as malicious by its sensors. & Binary \\ \hline Presence of URL shorteners & Presence of links with shorteners: a value of 1 is added if one or more links utilize this technology; otherwise, a value of 0 is added. & Binary \\ \hline Presence of out-of-domain Javascript content & Presence of scripts coded in JavaScript that are cross-domain. According to [93], these may be indicative of forced downloads and redirections, the execution of WebAssembly to disguade the user from remaining on the site, and the initiation of cryptojacking tasks. A value of 1 is added if they are present, and 0 if they are not. & Binary \\ \hline Presence of CAPTCHAs & In multimedia file download contexts, CAPTCHAs have three main roles: to shield hosts from download abuse, to bolster user confidence in completing the download, and to obscure content tracking by compelling crawlers to solve the puzzle. A value of 1 is added if CAPTCHAs are present, and 0 if not. & Binary \\ \hline Number of outbound and download links and iframes & Number of download links on site, ifames, redirection links, or shortening links. & Integer \\ \hline Access level & Proposed additional level based on the patterns observed by the web crawling sensor. & Discrete \([1-4]\) \\ \hline Title & The text title is included as a key feature since it reveals the website’s objective. & String \\ \hline Textual content & The textual content, found within the website’s DOM body tags, reveals the site’s nature. Content from JavaScript, JSON, and XML tags, or any non-visual elements, was excluded using regular expressions. & String \\ \hline Label & The labels provided according to the classification perspective are 0 for non-infringing, 1 for movies and series, 2 for music, 3 for software, and 4 for books. & Discrete \([1-4]\) \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Set of features suggested in this project. this, DF is adopted, which examines the tag graph and identifies nodes with content rich in words, marking them as visited once traversed. The algorithm delves deeper, marking both textual and irrelevant nodes through backtracking until no further elements remain to be explored. The result is a compilation of embedded text. Figure 8 provides a straightforward representation of how the DF approach locates embedded text within the HTML DOM of this project's dataset. Ultimately, before adding the title and text as plain text to the dataset, unnecessary white space, emoticons, and any characters outside of the American English UTF-8 encoding were removed, which can then be ingested and transformed by NLP techniques.