Improving WiFi Statistical Fingerprint-based Detection Techniques against UAV Stealth Attacks

The increasing popularity of low-cost aerial vehicles that can be remotely piloted by amateurs, is giving rise to a number of issues related to public safety and privacy. At the same time, the need for surveillance methods able to detect the presence of unauthorized drones meets the ubiquitous connectivity and pervasive technology typical of the big data era. This dualism drives the research community towards the design of monitoring systems able to integrate machine learning methods and data mining techniques in this evolving environment. In this framework, we consider a WiFi based approach aimed at detecting nearby unmanned aerial vehicles, by performing statistical fingerprint analysis on wireless traffic. We study the inherent vulnerabilities of the considered method through real-life experimental tests by setting up specific attack scenarios, and we devise and test a solution in order to improve the efficiency of the proposed technique in the presence of malicious countermeasures. Results show that the proposed improved detection technique is indeed robust to stealth attacks, and it is able to achieve very good recognition performance in different real-life testing scenarios.