  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 paper 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.
