Security Threats and Defensive Approaches in Machine Learning System Under Big Data Environment

Abstract

Under big data environment, machine learning has been rapidly developed and widely used. It has been successfully applied in computer vision, natural language processing, computer security and other application fields. However, there are many security problems in machine learning under big data environment. For example, attackers can add “poisoned” sample to the data source, and big data process system will process these “poisoned” sample and use machine learning methods to train model, which will directly lead to wrong prediction results. In this paper, machine learning system and machine learning pipeline are proposed. The security problems that maybe occur in each stage of machine learning system under big data processing pipeline are analyzed comprehensively. We use four different attack methods to compare the attack experimental results.The security problems are classified comprehensively, and the defense approaches to each security problem are analyzed. Drone-deploy MapEngine is selected as a case study, we analyze the security threats and defense approaches in the Drone-Cloud machine learning application envirolment. At last,the future development drections of security issues and challenages in the machine learning system are proposed.