Mitigating Label Flipping Attacks in Malicious URL Detectors Using
  Ensemble Trees
Ehsan Nowroozi Nada Jadalla Samaneh Ghelichkhani Alireza Jolfaei
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 to incorrect model behavior. Therefore, integrating
defense mechanisms into the architecture of ML models becomes an imperative
consideration to fortify against potential attacks.
  The focus of this study is on backdoor attacks in the context of URL
detection using ensemble trees. By illuminating the motivations behind such
attacks, highlighting the roles of attackers, and emphasizing the critical
importance of effective defense strategies, this paper contributes to the
ongoing efforts to fortify ML models against adversarial threats within the ML
domain in network security. We propose an innovative alarm system that detects
the presence of poisoned labels and a defense mechanism designed to uncover the
original class labels with the aim of mitigating backdoor attacks on ensemble
tree classifiers. We conducted a case study using the Alexa and Phishing Site
URL datasets and showed that LF attacks can be addressed using our proposed
defense mechanism. Our experimental results prove that the LF attack achieved
an Attack Success Rate (ASR) between 50-65% within 2-5%, and the innovative
defense method successfully detected poisoned labels with an accuracy of up to
100%.