Price Decision Support System Security - Features and Online Prediction Defense in Adversarial Environment

摘要

Price decision support systems (PDSS) are crucial for every big retailer in order to be able to decide about product prices in hundreds of stores and thousands of products. In this paper we identify, describe and formalize several price decision support system features that can be used as an input for machine learning algorithms, after that we select the features that can be exploited by potential attackers and discuss/evaluate the security issues of online learning features in adversarial environment PDSS. At the end we propose a kernel learning defense model for the sensitive features.