Privacy Preserving Machine Learning for Malicious URL Detection

Phishing remains the most prominent attack causing loss of billions of dollars for organizations and users every year. Attackers use phishing to obtain sensitive information from users, install malware and obtain control over their systems. Currently, web browsers counter this attack using blacklisting method, however it fails to detect newly generated malicious websites, hence ineffective. In the recent times, machine learning based URL classification techniques where trained models are deployed on server side, emerged as an effective solution to detect new malicious URLs and provide it as a service to the user. While malicious URL detection continues to be a problem, another potential concern is the user's query privacy (when offered as malicious URL detection as a service, where server can learn about the URL). Hence to address the query privacy, we propose privacy enabled malicious URL detection. In this work, we focus on privacy enabled malicious URL detection based on FHE using 3methods (i) Deep Neural Network (DNN) (ii) Logistic regression (iii) Hybrid. In the hybrid approach, the feature extraction is done using DNN and classification is done using logistic regression model, gives practical performance. We designed themodels based on split architecture (client/server). We present our experiments with the models trained using a dataset of 100,000 URLs (50,000 valid and 50,000 phished URLs). Our experiments show that malicious URL detection in encrypted domain is practical in terms of accuracy and efficiency.