Click fraud is a fast-growing cyber-criminal activity with the aim of deceptively clicking on the advertisements to make the proit to the publisher or cause loss to the advertiser. Due to the popularity of smartphones since the last decade, most of the modern-day advertisement businesses have been shifting their focus toward mobile platforms. Nowadays, in-app advertisement on mobile platforms is the most targeted victim of click fraud. Malicious entities launch attacks by clicking ads to artiicially increase the click rates of speciic ads without the intention of using them for legitimate purposes. The fraud clicks are supposed to be caught by the ad providers as part of their service to the advertisers; however, there is a lack of research in the current literature for addressing and evaluating different techniques of click fraud detection and prevention. Another challenge toward click fraud detection is that the attack model can itself be an active learning system (smart attacker) with the aim of actively misleading the training process of fraud detection model via polluting the training data. In this paper, we propose a deep-learning based model to address the challenges as mentioned above. The model is a hybrid of artiicial neural network (ANN), auto-encoder and semi-supervised generative adversarial network (GAN). Our proposed approach triumphs excellent accuracy than other models. CCS CONCEPTS· Information systems → Online advertising; · Security and privacy → Intrusion/anomaly detection and malware mitigation; · Computing methodologies → Machine learning.

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