Systematic Literature Review for Detecting Intrusions in Unmanned Aerial Vehicles Using Machine and Deep Learning

Abstract

Unmanned aerial vehicles (UAVs), known as drones, have significantly impacted the agricultural, police, military, and commercial sectors, aiming to enhance the quality of life; however, they are exposed to significant risks from the adversarial side, thereby gaining benefits from security vulnerabilities, including insecure communication channels, authorization risks, hardware, software, and network risks, to perform various attacks. One of those attacks is intrusion malware, which uses malicious programs, signal spoofing, denial of services, targeting integrity, confidentiality, and availability of the system. Detecting these intrusions has recently gained attention in academia and industrial fields for addressing existing threats and developing detection frameworks, such as utilizing machine and deep learning algorithms. Because of its importance in this field, this survey aims to provide a background for researchers interested in detecting malware in drones, discuss recent approaches, depict a taxonomy of constructing approaches, identify existing problems, and explore trends in future work.