  Industrial insider risk assessment using electroencephalogram (EEG) signals
has consistently attracted a lot of research attention. However, EEG
signal-based risk assessment systems, which could evaluate the emotional states
of humans, have shown several vulnerabilities to data poison attacks. In this
paper, from the attackers' perspective, data poison attacks involving
label-flipping occurring in the training stages of different machine learning
models intrude on the EEG signal-based risk assessment systems using these
machine learning models. This paper aims to propose two categories of
label-flipping methods to attack different machine learning classifiers
including Adaptive Boosting (AdaBoost), Multilayer Perceptron (MLP), Random
Forest, and K-Nearest Neighbors (KNN) dedicated to the classification of 4
different human emotions using EEG signals. This aims to degrade the
performance of the aforementioned machine learning models concerning the
classification task. The experimental results show that the proposed data
poison attacks are model-agnostically effective whereas different models have
different resilience to the data poison attacks.
