Year: 2024
Type: article
Abstract: In recent years, Machine Learning (ML) has brought about a significant revolution in several fields such as medicine, justice, cybersecurity, and other vital fields that require intelligent and urgent decision-making. With this development, a type of adversarial attack targeting ML models called a Poisoning Attack (PA) has emerged. One realistic attack scenario is for an adversary to subtly update samples or reverse some labels of training data, causing degradation to the model's overall accuracy during the testing phase. To gain a deeper understanding of this scenario, a survey will be conducted about the attack and how it is carried out against different models. In addition to the protection techniques to identify their weaknesses. Finally, some solutions will be proposed to maintain the availability, robustness, and integrity of ML models.
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
Funder: Mustansiriyah University
OpenAlex ID: W4406051489