Real Time Mobile Ad Investigator: An Effective and Novel Approach for Mobile Click Fraud Detection
Keywords:Mobile advertising, click fraud, classification, supervised learning, class imbalance, hidden Markov model
Today, mobile advertising is considered as the most effective medium to convey promotional messages to customers because of the excessive usage of mobile phones and tablets all around the world. However, this ecosystem has severely been affected by fraudulent activities due to a large sum of money circulated in the advertising industry. The term ad fraud is referred to as any kind of fraudulent activities that are executed by fraudulent users either a human or an automated script. The combat between researchers and fraudulent users never ends because more smarter strategies are being used by the fraudsters to bypass the significant number of detection and prevention solutions. The Real Time Mobile Ad Investigator-RTMAI is proposed as a software solution to address this problem where a novel supervised learning algorithm based on the hidden Markov model along with a rule engine have been proposed to classify fraudulent impressions in real time. Furthermore, RTMAI proposed a solution to address the class imbalance problem which is generic to most of the classification datasets. The experimental results show the significance of the proposed approach to classify the fraud or non-fraud clicks/events, impressions and even user sessions more confidently in real time.