Machine Learning-Driven Detection of Malicious URL: Comparative Analysis of Random Forest and SVMs - Adam, Hikmah Adwin; Nasution, Shaquil Fathza; Simanungkalit, Rikky Rifaldo; Diansyah, Ikhsan Hafid (JOURNAL OF INFORMATICS AND TELECOMMUNICATION ENGINEERING, 2024)
No longer a novelty, the internet has become the ubiquitous fabric of our lives, transforming how we interact, do business and disseminate information. However, its popularity has also attracted attackers who want to exploit it for personal gain. One tactic they use is to launch client-side attacks through malicious websites. Malicious websites are constantly evolving, and traditional methods such as blacklisting are no longer effective in identifying them. More sophisticated and adaptive solutions are needed to combat this threat. This research proposes an automatic malicious website detection method that utilizes URL properties and machine learning algorithms. This approach uses a combination of relevant URL features and a powerful machine learning model to accurately identify malicious websites. This research uses two popular machine learning algorithms: Random Forest (RF) and Support Vector Machines (SVM). Both models are trained on a dataset consisting of URL properties of malicious and Benign websites. The research results show that the proposed method is able to achieve a good level of accuracy in detecting malicious websites. Both RF and SVM show promising performance, with RF model achieved an accuracy of 86.15%, surpassing the SVM's performance of 85.38%. While overall performance is satisfactory, further optimization might be necessary, particularly to address potential class imbalance. Oversampling method could offer a more effective alternative to traditional undersampling methods and potentially improve performance across both website URLs categories