Securing a UAV using individual characteristics from an EEG signal

Abstract

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 under malicious attack conditions during which we implement a procedure where the UAV return safely to a 'home' position.

Review on EEG‐Based Authentication Technology

“…e key technology of key generation EEG-based biometric cryptosystems is the use of EEG signals to generate a unique and repeatable key. Some researchers have proved [79] to generate keys for encryption [80] or authentication [81]. e measured EEG biometrics has a high entropy across subjects.…”

“…Using BAN logic, the proposed authentication scheme was proved to be effective and practical [81]. Singandhupe et al [80] used a fuzzy extractor to generate a 128 bit AES encryption key from the EEG signal in the beta band.…”

“…Methods Key length (bits) Number of subjects Singandhupe et al [80] Fuzzy extractor 128 Damaševičius et al [82] Fuzzy commitment 400 42 Yang et al [83] Fuzzy commitment 21 10 Tuiri et al [84] Quantization 230 8 Bajwa and Dantu et al [76] Quantization 230 120 Nguyen et al [30] Quantization 256 3 Ravi et al [85] Quantization 62 10 improving the reliability of the system and the security of the biometric template. Albermany and Baqer [90] choose the C3, CZ, and C4 three channels of the left-hand and right-hand MI data from BCI Competition 2008-Graz data set A as their EEG data set.…”

A Distributed Control Framework of Multiple Unmanned Aerial Vehicles for Dynamic Wildfire Tracking

“…f (q, p 1 , p 2 , ..., p n )φ(q, t)dq, (9) where f (q, p 1 , p 2 , ..., p n ) represents some cost to cover a certain point q of the environment. The function φ(q, t), which is known as distribution density function, level of interestingness, or strategic importance, indicates the specific weight of the point q in that environment at time t. In this paper, the cost we are interested in is the quality of images when covering a spreading fire with a limited number of cameras.…”

“…UAV technology continues attracting a huge amount of research [8], [9]. Researchers developed controllers for UAVs to help them attain stability and effectiveness in completing their tasks [10].…”