Cross-Validation for Detecting Label Poisoning Attacks: A Study on Random Forest Algorithm

The widespread adoption of machine learning (ML) algorithms has revolutionized various aspects of modern life. However, their susceptibility to data poisoning attacks remains a significant concern due to their potential to compromise model integrity and performance. This study examines the impact of two types of data poisoning attacks on the Random Forest algorithm. It highlights the vulnerability of ML systems, especially in continual learning settings. We propose a simple yet effective strategy for continual learning ML systems to detect potential label poisoning attacks. This involves observing significant performance changes during model retraining. Experimental evaluation with Random Forest algorithms confirms the efficacy of the strategy in detecting and mitigating label poisoning attacks in continual learning systems.