Recently, Unmanned Aerial Vehicles (UAVs) are employed for a plethora of civilian applications. Such flying vehicles can accomplish tasks under the pilot's eyesight within the range of a remote controller, or autonomously according to a certain pre-loaded path configuration. Different path deviation attacks can be performed by malicious users against UAVs. We classify such attacks and the relative defenses based on the UAV's flight mode, i.e., (i) First Person View (FPV), (ii) civilian Global Navigation Satellite System based (GNSS), and (iii) GNSS "plus"auxiliary technologies (GNSS+), and on the multiplicity, i.e., (i) Single UAV, and (ii) Multiple UAVs. We found that very little has been done to secure the FPV flight mode against path deviation. In GNSS mode, spoofing is the most worrisome attack. The best defense against spoofing seems to be redundancy, such as adding vision chips to single UAV or using multiple arranged UAVs. No specific attacks and defenses have been found in literature for GNSS+ or for UAVs moving in group without a pre-ordered arrangement. These aspects require further investigation. @en

Cheap commercial off-the-shelf (COTS) drones have become widely available for consumers in recent years. Unfortunately, they also provide low-cost capabilities for attackers. Therefore, effective methods to detect the presence of non-cooperating rogue drones within a restricted area are highly required. Approaches based on detection of control traffic have been proposed but were not yet shown to work against other benign traffic, such as that generated by wireless security cameras. In this work, we propose a novel drone detection framework based on a Random Forest classification model. In essence, the framework leverages specific patterns in video traffic transmitted by drones. The patterns consist of repetitive synchronization packets (denoted as pivots) which we use as features in the proposed machine learning classifier. We show that our framework can achieve up to 99% detection accuracy over an encrypted WiFi channel using only 20 packets originated from the drone. Our system is able to identify drone transmissions even among very similar WiFi transmission (such as a security camera video stream) and in a noisy scenario with background traffic. @en