Reliable Security Algorithm for Drones Using Individual Characteristics From an EEG Signal

• Published in: IEEE access Vol. 6; pp. 22976 - 22986
• Main Authors: Singandhupe, Ashutosh, La, Hung Manh, Feil-Seifer, David
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 01.01.2018; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: advanced encryption standard (AES); Automatic pilots; Communication; Communications systems; Cybersecurity; DH-HEMTs; Drone aircraft; Drone vehicles; Drones; EEG signal; Electroencephalography; Encryption; Hash based algorithms; IEEE 802.15 Standard; Military applications; Polynomials; UAV; Unmanned aerial vehicles; Unmanned vehicles; Video data; xbee
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2018.2827362

Unmanned aerial vehicles (UAVs) have been applied for both civilian and military applications; scientific research involving UAVs has encompassed a wide range of scientific study. However, communication with unmanned vehicles are subject to attack and compromise. Such attacks have been reported as early as 2009, when a Predator UAV's video stream was compromised. Since UAVs extensively utilize autonomous behavior, it is important to develop an autopilot system that is robust to potential cyber-attack. In this paper, we present a biometric system to encrypt communication between a UAV and a computerized base station. This is accomplished by generating a key derived from a user's EEG Beta component. We first extract coefficients from Beta data using Legendre's polynomials. We perform encoding of the coefficients using Bose-Chaudhuri-Hocquenghem encoding and then generate a key from a hash function. The key is used to encrypt the communication between XBees. Also we have introduced scenarios where the communication is attacked. When communication with a UAV is attacked, a safety mechanism directs the UAV to a safe home location. This system has been validated on a commercial UAV under malicious attack conditions.