Not logged in.
Quick Search - Contribution
Contribution Details
| Type | Journal Article |
| Scope | Discipline-based scholarship |
| Title | Noise-based cyberattacks generating fake P300 waves in brain--computer interfaces |
| Organization Unit | |
| Authors |
|
| Item Subtype | Original Work |
| Refereed | Yes |
| Status | Published in final form |
| Language |
|
| Journal Title | Cluster Computing |
| Publisher | Springer |
| Geographical Reach | international |
| ISSN | 1386-7857 |
| Volume | 25 |
| Number | 1 |
| Page Range | 33 - 48 |
| Date | 2022 |
| Abstract Text | Most of the current Brain–Computer Interfaces (BCIs) application scenarios use electroencephalographic signals (EEG) containing the subject’s information. It means that if EEG were maliciously manipulated, the proper functioning of BCI frameworks could be at risk. Unfortunately, it happens in frameworks sensitive to noise-based cyberattacks, and more efforts are needed to measure the impact of these attacks. This work presents and analyzes the impact of four noise-based cyberattacks attempting to generate fake P300 waves in two different phases of a BCI framework. A set of experiments show that the greater the attacker’s knowledge regarding the P300 waves, processes, and data of the BCI framework, the higher the attack impact. In this sense, the attacker with less knowledge impacts 1% in the acquisition phase and 4% in the processing phase, while the attacker with the most knowledge impacts 22% and 74%, respectively. |
| Free access at | DOI |
| Digital Object Identifier | 10.1007/s10586-021-03326-z |
| Other Identification Number | merlin-id:23184 |
| PDF File |
Download from ZORA
|
| Export |
BibTeX
EP3 XML (ZORA)
|