  Unmanned Aerial Vehicles (UAVs) are becoming more dependent on mission
success than ever. Due to their increase in demand, addressing security
vulnerabilities to both UAVs and the Flying Ad-hoc Networks (FANET) they form
is more important than ever. As the network traffic is communicated through
open airwaves, this network of UAVs relies on monitoring applications known as
Intrusion Detection Systems (IDS) to detect and mitigate attacks. This paper
will survey current IDS systems that include machine learning techniques when
combating various vulnerabilities and attacks from bad actors. This paper will
be concluded with research challenges and future research directions in finding
an effective IDS system that can handle cyber-attacks while meeting performance
requirements.
