A Secure Real-time Multimedia Streaming through Robust and Lightweight AES Encryption in UAV Networks for Operational Scenarios in Military Domain

multimodal data encryption and decryption for security applications in protected environments like espionage, situational awareness, monitoring, and counter-UAV. Data is captured from drones equipped with microphone arrays and cameras. This is performed by exploiting acoustic event analysis, video tracking, and recognition, performed on a ground station. All the communications are delivered in a secure data channel. Integrity and secrecy of the sensitive data acquired by drones must be guaranteed until the data is delivered in real-time from UAVs to the destination node. A possible data exploit may cause critical problems if the data is intercepted by malicious attackers. Being the drones equipped with low energy consuming devices with low computational power, like single-board-computers, a real-time lightweight application-level AES encryption, in addition to the MAC encryption of the wireless communication channel, has been considered. In the experiment, the encryption and decryption process has been optimized, even under adverse transmission conditions ensuring continuous data encryption even if some packets are lost or the connection is repeatedly dropped and reestablished

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

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