Decision Making Support System for Managing Advertisers By Ad Fraud Detection

Abstract Efficient lead management allows substantially enhancing online channel marketing programs. In the paper, we classify website traffic into human- and bot-origin ones. We use feedforward neural networks with embedding layers. Moreover, one-hot encoding for categorical data. The data of mouse clicks come from seven large retail stores classification three financial institutions. are collected by a JavaScript code embedded HTML pages. proposed models achieved relatively high accuracy in detecting artificially generated traffic.