A Method to Detect Threat in Advertisement URL and its Content

Potential harms of malicious websites can be mitigated by a threat detection model i.e., classifying URL website by analyzing its content and meta-data. Online advertisements network large part of malicious content across the web. We propose a threat detection model specialized to safeguard against the potential harms engendered by malicious web advertisements. Our approach integrates diverse analytical methods, encompassing JavaScript analysis to target malicious routines and threat detection model for classifying URL as malicious or authentic by amalgamating distinct yet complementary streams of features from HTML and RDAP. Our model achieves unassailability with 90.71% accuracy in identifying and mitigating threats.