Malicious advertising URL; Adversarial Attacks; Adversarial Learning; Adversarial Machine Learning; Cybersecurity; web-scrapped features; Ensemble Trees

The goal of our research is to identify malicious advertisement URLs and to apply adversarial attack on ensembles. We extract lexical and web-scrapped features from using python code. And then 4 machine learning algorithms are applied for the classification process and then used the K-Means clustering for the visual understanding. We check the vulnerability of the models by the adversarial examples. We applied Zeroth Order Optimization adversarial attack on the models and compute the attack accuracy.

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