A Survey on Poisoning Attacks Against Supervised Machine Learning
It addresses security concerns for AI practitioners by synthesizing knowledge on adversarial threats, but is incremental as it reviews existing literature without new empirical results.
This survey paper compiles and categorizes existing research on poisoning attacks against supervised machine learning models, summarizing methodologies and limitations to highlight vulnerabilities in AI systems.
With the rise of artificial intelligence and machine learning in modern computing, one of the major concerns regarding such techniques is to provide privacy and security against adversaries. We present this survey paper to cover the most representative papers in poisoning attacks against supervised machine learning models. We first provide a taxonomy to categorize existing studies and then present detailed summaries for selected papers. We summarize and compare the methodology and limitations of existing literature. We conclude this paper with potential improvements and future directions to further exploit and prevent poisoning attacks on supervised models. We propose several unanswered research questions to encourage and inspire researchers for future work.