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.

翻译：无人机（UAV）比以往任何时候都更依赖任务的成功。随着对无人机及其构成的飞行自组网（FANET）需求的增长，解决两者安全漏洞的重要性与日俱增。由于网络流量通过开放空中信道传输，这种无人机网络需依赖名为入侵检测系统（IDS）的监控应用程序来检测并缓解攻击。本文综述了当前采用机器学习技术应对恶意行为者各种漏洞与攻击的IDS系统。最后总结研究挑战，并探讨在满足性能要求的同时有效应对网络攻击的IDS系统未来研究方向。