| Title | The Adversarial UFP/UFN Attack: A New Threat to ML-based Fake News Detection Systems? |
| Publication Type | Conference Paper |
| Year of Publication | 2020 |
| Authors | Brown, Brandon, Richardson, Alexicia, Smith, Marcellus, Dozier, Gerry, King, Michael C. |
| Conference Name | 2020 IEEE Symposium Series on Computational Intelligence (SSCI) |
| Date Published | dec |
| Keywords | Computer science, Fake News Detection Systems, feature extraction, Measurement, privacy, pubcrawl, Radio frequency, social networking (online), software engineering, Support vector machines, threat vectors, Universal False Negative, Universal False Positive, Voting |
| Abstract | In this paper, we propose two new attacks: the Adversarial Universal False Positive (UFP) Attack and the Adversarial Universal False Negative (UFN) Attack. The objective of this research is to introduce a new class of attack using only feature vector information. The results show the potential weaknesses of five machine learning (ML) classifiers. These classifiers include k-Nearest Neighbor (KNN), Naive Bayes (NB), Random Forrest (RF), a Support Vector Machine (SVM) with a Radial Basis Function (RBF) Kernel, and XGBoost (XGB). |
| DOI | 10.1109/SSCI47803.2020.9308298 |
| Citation Key | brown_adversarial_2020 |
The Adversarial UFP/UFN Attack: A New Threat to ML-based Fake News Detection Systems?