Preventing Data Poisoning Attacks By Using Generative Models

Abstract

At the present time, machine learning methods have been becoming popular and the usage areas of these methods have also increased with this popularity. The machine learning methods are expected to increase in the cyber security components like firewalls, antivirus software etc. Nowadays, the use of this type of machine learning methods brings with it various risks. Attackers develop different methods to manipulate different systems, not only cyber security components, but also image detection systems. Therefore, securing machine learning models has become critical. In this paper, we demonstrate a data poisoning attack towards classification method of machine learning models and we also proposed a defense algorithm which makes machine learning models more robust against data poisoning attacks. In this study, we have conducted data poisoning attacks on MNIST, a widely used character detection data set. Using the poisoned MNIST dataset, we built classification models more reliable by using a generative model such as AutoEncoder.