Learning Tensor-Based Representations from Brain-Computer Interface Data for Cybersecurity

Understanding, modeling, and explaining neural data is a challenging task. In this paper, we learn tensor-based representations of electroencephalography (EEG) data to classify and analyze the underlying neural patterns related to phishing detection tasks. Specifically, we conduct a phishing detection experiment to collect the data, and apply tensor factorization to it for feature extraction and interpretation. Traditional feature extraction techniques, like power spectral density, autoregressive models, and Fast Fourier transform, can only represent data either in spatial or temporal dimension; however, our tensor modeling leverages both spatial and temporal traits in the input data. We perform a comprehensive analysis of the neural data and show the practicality of multi-way neural data analysis. We demonstrate that using tensorbased representations, we can classify real and phishing websites with accuracy as high as 97%, which outperforms state-of-the-art approaches in the same task by 21%. Furthermore, the extracted latent factors are interpretable, and provide insights with respect to the brain's response to real and phishing websites.