Design and Implementation of a Machine Learning-Based Malicious URL Detection System

Abstract

The internet has rapidly evolved in its communication, commerce and information sharing making it a huge platform for cyber threats, particularly malicious URLs. They pose a serious threat to individuals and to organisations. Phishing attacks, malware distribution and other types of cybercrime frequently are carried out through malicious URLs. In this research, we have created and tested the machine learning models to detect malicious URLs. The labeled URLs used were obtained from a public dataset with more than 651,000 labeled URLs. The dataset was prepared for classification by applying data pre-processing techniques like stratified sampling, label encoding and Term Frequency–Inverse Document Frequency (TF-IDF) vectorization. To train and test the algorithms, five machine learning were used: Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Naïve Bayes (NB), Random Forest (RF) and Extreme Gradient Boosting (XGBoost), which were trained and evaluated by the metrics of accuracy, precision, recall and F1 score. The results indicated that the Random Forest model had the highest classification accuracy (95%) as compared to the other models. Moreover, a web based malicious URL detection system was developed to demonstrate the actual application of the developed models in real time cyber security scenarios. The study provides a conclusion that the machine learning techniques, particularly ensemble learning techniques can be considered an effective and reliable technique to detect malicious URL.