(2021) COTS Drone Detection Using Video Streaming Characteristics.
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Abstract
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.
| Item Type: | Conference or Workshop Item (A Paper) (Paper) |
|---|---|
| Divisions: | Nils Ole Tippenhauer (SCy-Phy) |
| Conference: | ICDCN International Conference on Distributed Computing and Networking |
| Depositing User: | Nils Ole Tippenhauer |
| Date Deposited: | 22 Jan 2021 09:59 |
| Last Modified: | 15 Oct 2022 14:10 |
| Primary Research Area: | NRA3: Threat Detection and Defenses |
| URI: | https://publications.cispa.saarland/id/eprint/3348 |
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