TY - GEN
T1 - Cybersecurity Framework for P300-based Brain Computer Interface
AU - Belkacem, Abdelkader Nasreddine
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/10/11
Y1 - 2020/10/11
N2 - This paper describes a cybersecurity framework for protecting brain computer interface (BCI) technology. This framework consists of cybersecurity risk scenarios related to user safety/privacy and best practices to manage them. This framework provides solutions for privacy and safety issues of the existing noninvasive BCIs (e.g., electroencephalography (EEG)-based BCI). We chose to design a P300-based BCI application because it is the most popular modality, simulate some common cybersecurity attacks, and find a relevant solution to protect the user and/or integrated EEG hardware-software system. In this paper, we describe how cybersecurity risks could affect BCI form streaming/recording EEG signal in real-time until sending commands. We used EEG Equipment for measuring brain activity and Python programing language to build our experimental paradigm, record EEG signal, classify P300 components, send a message to another user, simulate some attacks, and find perfect solutions for assuring high BCI protection. This paper gives an overview of the framework, some description of BCI hacking challenges and their impact on BCI users as well as a preliminary demonstration of a P300-based BCI system with two common simple attacks.
AB - This paper describes a cybersecurity framework for protecting brain computer interface (BCI) technology. This framework consists of cybersecurity risk scenarios related to user safety/privacy and best practices to manage them. This framework provides solutions for privacy and safety issues of the existing noninvasive BCIs (e.g., electroencephalography (EEG)-based BCI). We chose to design a P300-based BCI application because it is the most popular modality, simulate some common cybersecurity attacks, and find a relevant solution to protect the user and/or integrated EEG hardware-software system. In this paper, we describe how cybersecurity risks could affect BCI form streaming/recording EEG signal in real-time until sending commands. We used EEG Equipment for measuring brain activity and Python programing language to build our experimental paradigm, record EEG signal, classify P300 components, send a message to another user, simulate some attacks, and find perfect solutions for assuring high BCI protection. This paper gives an overview of the framework, some description of BCI hacking challenges and their impact on BCI users as well as a preliminary demonstration of a P300-based BCI system with two common simple attacks.
KW - BCI privacy and safety issues
KW - Brain computer interface (BCI)
KW - Cyber-attacks
KW - Cybersecurity
KW - P300-based BCI
UR - http://www.scopus.com/inward/record.url?scp=85098866100&partnerID=8YFLogxK
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U2 - 10.1109/SMC42975.2020.9283100
DO - 10.1109/SMC42975.2020.9283100
M3 - Conference contribution
AN - SCOPUS:85098866100
T3 - Conference Proceedings - IEEE International Conference on Systems, Man and Cybernetics
SP - 1
EP - 6
BT - 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2020
Y2 - 11 October 2020 through 14 October 2020
ER -