  Malicious advertisement URLs pose a security risk since they are the source
of cyber-attacks, and the need to address this issue is growing in both
industry and academia. Generally, the attacker delivers an attack vector to the
user by means of an email, an advertisement link or any other means of
communication and directs them to a malicious website to steal sensitive
information and to defraud them. Existing malicious URL detection techniques
are limited and to handle unseen features as well as generalize to test data.
In this study, we extract a novel set of lexical and web-scrapped features and
employ machine learning technique to set up system for fraudulent advertisement
URLs detection. The combination set of six different kinds of features
precisely overcome the obfuscation in fraudulent URL classification. Based on
different statistical properties, we use twelve different formatted datasets
for detection, prediction and classification task. We extend our prediction
analysis for mismatched and unlabelled datasets. For this framework, we analyze
the performance of four machine learning techniques: Random Forest, Gradient
Boost, XGBoost and AdaBoost in the detection part. With our proposed method, we
can achieve a false negative rate as low as 0.0037 while maintaining high
accuracy of 99.63%. Moreover, we devise a novel unsupervised technique for data
clustering using K- Means algorithm for the visual analysis. This paper
analyses the vulnerability of decision tree-based models using the limited
knowledge attack scenario. We considered the exploratory attack and implemented
Zeroth Order Optimization adversarial attack on the detection models.
