A Survey on the Jamming and Spoofing attacks on the Unmanned Aerial Vehicle Networks

Recently, unmanned aerial vehicles (UAVs) have attracted the attention of companies, governments, and organizations to execute strategic missions. The security of UAVs network has become an urgent need to avoid disastrous consequences. For example, jamming and spoofing attacks are the most dangerous attacks used frequently against UAV networks. The jamming attack is a stealthy attack that is hard to detect and leads to the unavailability of the service over the wireless network. In some cases, non-malicious interference and congestion in the network are similar to a jamming attack. Therefore, an efficient anomaly detection technique must distinguish behaviors in the drone’s wireless network as normal or abnormal behavior. Furthermore, the spoofing attack targets the Global Position System (GPS) signal to manipulate it and deviate the UAV to the adversary’s extreme zones. Some detection methods used to defend against GPS spoofing attacks are classified into three categories: digital signatures, encryption, and some characteristics of Automatic Dependable Surveillance-Broadcast (ADS-B). This article reviews the impact of the jamming and spoofing attack on the UAV wireless network and the previous conventional security detection and defense mechanism. Additionally, it addresses the benefit of deep learning technology to show how it is effective when it applies to protecting UAV networks.