Cyberattacks on Miniature Brain Implants to Disrupt Spontaneous Neural Signaling

Sergio Lopez Bernal, Alberto Huertas Celdran, Lorenzo Fernandez Maimo, Michael Taynnan Barros, Sasitharan Balasubramaniam, Gregorio Martinez Perez

Research output: Contribution to journalArticlepeer-review

6 Citations (Scopus)

Abstract

Brain-Computer Interfaces (BCI) arose as systems that merge computing systems with the human brain to facilitate recording, stimulation, and inhibition of neural activity. Over the years, the development of BCI technologies has shifted towards miniaturization of devices that can be seamlessly embedded into the brain and can target single neuron or small population sensing and control. We present a motivating example highlighting vulnerabilities of two promising micron-scale BCI technologies, demonstrating the lack of security and privacy principles in existing solutions. This situation opens the door to a novel family of cyberattacks, called neuronal cyberattacks, affecting neuronal signaling. This article defines the first two neural cyberattacks, Neuronal Flooding (FLO) and Neuronal Scanning (SCA), where each threat can affect the natural activity of neurons. This work implements these attacks in a neuronal simulator to determine their impact over the spontaneous neuronal behavior, defining three metrics: number of spikes, percentage of shifts, and dispersion of spikes. Several experiments demonstrate that both cyberattacks produce a reduction of spikes compared to spontaneous behavior, generating a rise in temporal shifts and a dispersion increase. Mainly, SCA presents a higher impact than FLO in the metrics focused on the number of spikes and dispersion, where FLO is slightly more damaging, considering the percentage of shifts. Nevertheless, the intrinsic behavior of each attack generates a differentiation on how they alter neuronal signaling. FLO is adequate to generate an immediate impact on the neuronal activity, whereas SCA presents higher effectiveness for damages to the neural signaling in the long-term.

Original languageEnglish
Article number9169881
Pages (from-to)152204-152222
Number of pages19
JournalIEEE Access
Volume8
DOIs
Publication statusPublished - 2020

Keywords

  • artificial neural networks
  • biological neural networks
  • Brain computer interfaces
  • security

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