A cybersecure P300-based brain-to-computer interface against noise-based and fake P300 cyberattacks

Mezzina, G., Annese, V. F. and De Venuto, D. (2021) A cybersecure P300-based brain-to-computer interface against noise-based and fake P300 cyberattacks. Sensors, 21(24), 8280. (doi: 10.3390/s21248280)

Abstract

In a progressively interconnected world where the internet of things (IoT), ubiquitous computing, and artificial intelligence are leading to groundbreaking technology, cybersecurity remains an underdeveloped aspect. This is particularly alarming for brain-to-computer interfaces (BCIs), where hackers can threaten the user’s physical and psychological safety. In fact, standard algorithms currently employed in BCI systems are inadequate to deal with cyberattacks. In this paper, we propose a solution to improve the cybersecurity of BCI systems. As a case study, we focus on P300-based BCI systems using support vector machine (SVM) algorithms and EEG data. First, we verified that SVM algorithms are incapable of identifying hacking by simulating a set of cyberattacks using fake P300 signals and noise-based attacks. This was achieved by comparing the performance of several models when validated using real and hacked P300 datasets. Then, we implemented our solution to improve the cybersecurity of the system. The proposed solution is based on an EEG channel mixing approach to identify anomalies in the transmission channel due to hacking. Our study demonstrates that the proposed architecture can successfully identify 99.996% of simulated cyberattacks, implementing a dedicated counteraction that preserves most of BCI functions.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Annese, Dr Valerio

Creator Roles

Annese, V. F.: Conceptualization, Methodology, Validation, Investigation, Data curation, Writing – original draft, Writing – review and editing, Visualization

• Authors: Mezzina, G., Annese, V. F., and De Venuto, D.
• College/School: College of Science and Engineering > School of Engineering > Electronics and Nanoscale Engineering
• Journal Name: Sensors
• Publisher: MDPI
• ISSN: 1424-8220
• ISSN (Online): 1424-8220
• Published Online: 10 December 2021
• Copyright Holders: Copyright © 2021 The Authors
• First Published: First published in Sensors 21(24): 8280
• Publisher Policy: Reproduced under a Creative Commons License