@inproceedings{9488bf54487445fa9c38f9d907455618,
title = "The Effect of Label-Flipping attack on Different Mobile Machine Learning Classifiers",
abstract = "AI technology is widely used in different fields due to the effectiveness and accurate results that have been achieved. The diversity of usage attracts many attackers to attack AI systems to reach their goals. One of the most important and powerful attacks launched against AI models is the label-flipping attack. This attack allows the attacker to compromise the integrity of the dataset, where the attacker is capable of degrading the accuracy of ML models or generating specific output that is targeted by the attacker. Therefore, this paper studies the robustness of several Machine Learning models against targeted and non-targeted label-flipping attacks against the dataset during the training phase. Also, it checks the repeatability of the results obtained in the existing literature. The results are observed and explained in the domain of the cyber security paradigm.",
keywords = "Artificial Inelegant (AI), Blockchain, label-flipping attack, Machine learning (ML), poisoning attack, Support vector machine (SVM)",
author = "Alanoud Almemari and Raviha Khan and Yeun, {Chan Yeob}",
note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023 ; Conference date: 07-03-2023 Through 08-03-2023",
year = "2023",
doi = "10.1109/ICBATS57792.2023.10111479",
language = "British English",
series = "2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023",
address = "United States",
}