Decision Making Support System for Managing Advertisers By Ad Fraud Detection
Marcin Gabryel, Magdalena Scherer, Łukasz Sułkowski, Robertas Damaševičius
Abstract
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, we use one-hot encoding for categorical data. The data of mouse clicks come from seven large retail stores and the data of lead classification from three financial institutions. The data are collected by a JavaScript code embedded into HTML pages. The three proposed models achieved relatively high accuracy in detecting artificially generated traffic.
Topics & Concepts
Categorical variableComputer scienceJavaScriptEmbeddingCode (set theory)Encoding (memory)Feed forwardData miningMachine learningArtificial intelligenceWorld Wide WebEngineeringSet (abstract data type)Control engineeringProgramming languageSpam and Phishing DetectionConsumer Market Behavior and PricingInternet Traffic Analysis and Secure E-voting