# Securing the Smart City Airspace: Drone Cyber Attack Detection through Machine Learning
###### Abstract  
Drones are increasingly adopted to serve a smart city through their ability to render quick and adaptive services. They are also known as unmanned aerial vehicles (UAVs) and are deployed to conduct area surveillance, monitor road networks for traffic, deliver goods and observe environmental phenomena. Cyber threats posed through compromised drones contribute to sabotage in a smart city's airspace, can prove to be catastrophic to its operations, and can also cause fatalities. In this contribution, we propose a machine learning-based approach for detecting hijacking, GPS signal jamming and denial of service (DoS) attacks that can be carried out against a drone. A detailed machine learning-based classification of drone datasets for the DJI Phantom 4 model, compromising both normal and malicious signatures, is conducted, and results obtained yield advisory to foster futuristic opportunities to safeguard a drone system against such cyber threats. drones; criminal activity; machine learning; cyber attacks +
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The benefits to both civilian as well as defence applications through adoption of drones are aplenty. For instance, monitoring of road traffic in a city grid through static cameras is limited by the scope of data collection. On the contrary, a drone can fly across a city's aerospace and gather data from numerous locales to help the city administrators identify traffic congestion points and take necessary action to enable smooth traffic flow. Such live feeds of data can be transmitted by the drone to a central smart city cloud service center for further processing (can be routed via the drone's Internet-connected ground controller). Smart city curb-side sensors can also be integrated with a central smart city facility comprising the cloud, drones, IoT sensors as well as smart vehicles, to facilitate a fully integrated platform of heterogeneous objects. Such a facility can be harnessed to enable the identification of available parking spots (rendered to a smart vehicle in real-time), identify emergency zones, which can be avoided by vehicles and assess threats from weather phenomena (including heavy rainfalls and tornado warnings) [4]. The benefits of having drones as part of a smart city landscape are aplenty, as described above. However, there is no holistic and comprehensive framework in place to identify, prevent or even detect cyber threats that are posed through the introduction of drones in a smart city's airspace. The intrusion of drones into no-fly zones poses a threat to public safety, compromised of a secure premise including penetrating an airport's airspace and posing threats to aircraft and airport operations, and the dropping of illegal goods (including delivery of unlawful products to prisons). Research and development in this space have seen significant advancement in recent times. For instance, Dedrone has proposed an AI-enabled portable drone detection unit (tower) for detecting unauthorized drone intrusions into no-fly zones through deployment of such monitoring towers at specific locales [5]. Whilst ongoing research in the domain of cyber attacks that involve drones is expanding very quickly, there is still a need to identify drone-based cyber attacks, assess the types of threats posed on a smart city's airspace and the impact of a drone-based attack to a city's economy. In this research, we present a novel scheme for identifying malicious drone behavior through a deep analysis of routine (normal) drone operations when obtained from actual drone flight data, reverse engineering of patterns of normal drone flight behavior, synthesis of drone attack traffic, specifically for hijacking GPS signal jamming and DoS attacks and the adoption of machine learning techniques to classify the synthesized drone data into normal or malicious. The rest of the paper is organized as follows. Section 2 provides a background to the study, including a discussion on the various types of threat models for drones.