# Kashif: A Chrome Extension for Classifying Arabic Content on Web Pages Using Machine Learning
###### Abstract  
Search engines are significant tools for finding and retrieving information. Every day, many new web pages in various languages are added. The threats of cyberattacks are expanding rapidly with this massive volume of data. The majority of studies on the detection of malicious websites focus on English-language websites. This necessitates more studies on malicious detection on Arabic-content websites. In this research, we aimed to investigate the security of Arabic-content websites by developing a detection tool that analyzes Arabic content based on artificial intelligence (AI) techniques. We contributed to the field of cybersecurity and AI by building a new dataset of 4048 Arabic-content websites. We created and conducted a comparative performance evaluation for four different machine-learning (ML) models using feature extraction and selection techniques: extreme gradient boosting, support vector machines, decision trees, and random forests. The best-performing model was then integrated into a Chrome plugin, created based on a random forest (RF) model, and utilized the features selected via the chi-square technique. This produced plugin tool attained an accuracy of 92.96% for classifying Arabic-content websites as phishing, suspicious, or benign. To our knowledge, this is the first tool designed specifically for Arabic-content websites.  
artificial intelligence; machine learning; random forest; malicious; phishing; benign Article  
Kashif: A Chrome Extension for Classifying Arabic Content on Web Pages Using Machine Learning  
[PERSON], [PERSON], [PERSON], [PERSON].  
information, reputational damage, financial loss, or malware infections [3]. There are several techniques used to identify these malicious URLs, falling under the categories of blacklisting, machine learning (ML), and deep learning (DL).  
Over the past few years, artificial intelligence (AI) techniques have become more prevalent in cybersecurity domains, significantly aiding in identifying and mitigating a wide range of threats and attacks [4]. Machine learning (ML) is a branch of AI in which researchers attempt to provoke a system or computer to learn from previous data observations to improve its performance in a particular task [5]. Deep learning (DL) is a subfield of ML that deals with artificial neural networks (ANNs), which are algorithms inspired by the structure and function of the brain [6]. ML and DL have been widely used in cybersecurity applications in recent years, such as intrusion detection and biometric-based user authentication. Such intelligent methods are increasingly being applied to analyze data to predict the future and derive significant insights that aid decision-making [7].  
In this research, we used AI models to create a malicious web page detection tool that is available as a Chrome browser extension. The tool was built based in the following phases:  
1. Collecting a dataset that contains Arabic-content URLs in three categories, phishing, suspicious, or benign, using a crawling tool;
2. Pre-processing this dataset and extracting the important features of the URLs;
3. Building different ML models and conducting a comparative performance evaluation;
4. Creating a tool based on the best model that delivers the highest accuracy in the shortest time.  
The rest of this paper is structured as follows: Section 2 presents the related work from other studies. Section 3 presents our research methodology, covering all related stages. Section 4 presents the results yielded by the ML models. Finally, we conclude and discuss our future work in Section 5.