Securing a UAV Using Individual Characteristics From an EEG Signal
Ashutosh Singandhupe, Hung Manh La, David Feil-Seifer, Pei Huang,, Linke Guo, and Ming Li

TL;DR
This paper proposes a biometric-based encryption system using EEG signals to secure UAV communication, enabling the UAV to respond safely during cyber attacks by returning to a designated home position.
Contribution
It introduces a novel EEG-based biometric key generation method for UAV communication security and a safety mechanism for attack response.
Findings
The EEG-based system successfully encrypts UAV communication.
The UAV can return safely to home during malicious attacks.
The biometric system enhances UAV cybersecurity measures.
Abstract
Unmanned aerial vehicles (UAVs) have gained much attention in recent years for both commercial and military applications. The progress in this field has gained much popularity and the research has encompassed various fields of scientific domain. Cyber securing a UAV communication has been one of the active research field since the attack on Predator UAV video stream hijacking in 2009. Since UAVs rely heavily on on-board autopilot to function, it is important to develop an autopilot system that is robust to possible cyber attacks. In this work, we present a biometric system to encrypt the UAV communication by generating a key which is derived from Beta component of the EEG signal of a user. We have developed a safety mechanism that would be activated in case the communication of the UAV from the ground control station gets attacked. This system has been validated on a commercial UAV…
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Taxonomy
TopicsDistributed Control Multi-Agent Systems · UAV Applications and Optimization · Advanced Memory and Neural Computing
