# Kashif: A Chrome Extension for Classifying Arabic Content on Web Pages Using Machine Learning
## 2 Related Work  
Many studies have been conducted on detecting malicious URLs for English-content pages. Unlike Arabic-content pages, there is a lack of studies on this topic. As AI continues to grow, ML techniques are becoming more enhanced and proving their efficiency in many fields. ML techniques are used widely, especially in the detection of malicious websites. The studies in one review paper [8] used ML to detect Arabic-content pages and English-content pages. Based on these studies, no applicable and real-time tool has been used as a detection model to classify Arabic-content websites.  
Several studies developed an extension tool that detects malicious English web page URLs. [PERSON] et al. [9] developed a Chrome extension using the extreme gradient boosting (XGBoost 1.6.1) model and VirusTotal to detect malicious websites the user visited and determine whether the downloaded files from the Internet were safe or malicious. They used a dataset from the UCI Repository [10]. The XGBoost classifier yielded an accuracy of 96.69%.  
The study conducted by [PERSON] et al. [11] proposed a Chrome extension using a DL classifier. The dataset was collected from search engines, CommonCrawl [12], and PhishTank [13]. They used recurrent neural networks (RNNs), long short-term memory (LSTM), and an ANN. The LSTM model achieved the best results, with an accuracy of 96.89%.  
Moreover, [PERSON] et al. [14] developed a Chrome extension to detect phishing sites and aid in preventing phishing attacks. The UCI Machine Learning repository and PhishTank were two dataset sources used. In total, 16 out of 30 lexical, content, and network features were selected. To implement this extension, a support vector machine (SVM)-trained persistent model was employed to detect malicious sites. The SVM attained superior results compared to random forests and artificial neural networks, with a 90.05% accuracy.  
Furthermore, [PERSON] [15] proposed a browser extension that end users might utilize while browsing. This paper utilized 2000 legitimate and phishing URLs from Majestic Million [16] and Phishtank [13]. To enable the ML model to classify the URLs, 23 lexical and content-based features were extracted. The collected features were fed into several ML classifiers, such as RF, categorical boosting (CatBoost), XGBoost, multilayer perceptron (MLP), naive Bayes, and logistic regression (LR). The classifiers' performances were compared, and RF outperformed the others with an accuracy of 95%.  
Lastly, [PERSON] et al. [17] developed an ML model that can distinguish between legitimate and phishing web pages. They obtained 6158 phishing URLs and 4896 legitimate URLs from phishtank.com [13]. Moreover, 30 lexical, content-based, and network-based features were retrieved from URLs and fed into various ML models. They utilized various ML models, including the gradient-boosting classifier (GBC), decision tree (DT), SVM, and RF. The classifiers' performances were compared, and the GBC had the better detection accuracy, which was 97.4%.  
More research is needed to detect phishing attacks targeting Arabic-content websites. For instance, [PERSON] et al. [18] demonstrated the effectiveness of Google's anti-spamming methods against web-spam pages containing non-English content. It offered a solution in the form of a browser anti-spam plug-in capable of detecting Arabic spam pages. The authors themselves assembled the dataset that was used. They chose seven content-based features. They also evaluated four ML methods by building multiple variations of their classifier. The highest detection rate achieved by RF was 87.13%.  
The limited amount of research dedicated to Arabic-content websites highlights the need for more extensive studies, the creation of new datasets, and the development of specialized tools tailored to addressing the increasing threats within this field.