Mitigating Label Flipping Attacks in Malicious URL Detectors Using Ensemble Trees

arXiv:2403.02995v1 Announce Type: cross
Abstract: Malicious URLs provide adversarial opportunities across various industries, including transportation, healthcare, energy, and banking which could be detrimental to business operations. Consequently, the detection of these URLs is of crucial importance; however, current Machine Learning (ML) models are susceptible to backdoor attacks. These attacks involve manipulating a small percentage of training data labels, such as Label Flipping (LF), which changes benign labels to malicious ones and vice versa. This manipulation results in misclassification and leads …

abstract adversarial arxiv attacks backdoor banking business business operations cs.ai cs.cr cs.cy cs.lg cs.ni current detection energy ensemble healthcare importance industries machine machine learning operations opportunities transportation trees type url urls