IMPLEMENTASI EKSTENSI GOOGLE CHROME DALAMMENDETEKSI SITUS WEB PHISHING MENGGUNAKANALGORITMA RANDOM FOREST - Muhamad Abdul Ghanni Al Ghifari (2022)
In this technological age, the use of the internet has made people's daily lives easier. With all the benefits of the internet, there are certain people who commit various kinds of crimes, one of which is creating phishing websites. Many victims unknowingly enter their confidential data into phishing websites because the domain URL, content, appearance and others are made as similar as possible to the original website. Because of this, this research proposes phishing website detection. To detect phishing websites, the most commonly used approach is the blacklist and whitelist method, but this method has some drawbacks, namely not all URLs or newly created URLs are directly in the database. Therefore, this research aims to use a machine learning approach, namely the Random Forest method, by implementing it into a browser extension such as Google Chrome. This browser extension extracts features from URLs and web pages only, without relying on web services, so that detection can be faster. The classification model evaluation results have an accuracy of 90.2%, recall 88.8% and precision 88.8%. After the model was implemented into the browser extension, a performance evaluation was carried out using new data with an accuracy of 88%, recall 84% and precision of 91.3%, which decreased performance but was still quite good compared to the default phishing website detection from Google Safe Browsing (GSB) on Google Chrome, which has an average accuracy of ~45%.