The topic of Unmanned Aerial Vehicle (UAV) cybersecurity has received significant recent interest from the research community, with many methods proposed in the literature to improve detect and mitigate various types of attacks. This paper provides a comprehensive review of UAV cybersecurity, addressing all aspects of the UAV ecosystem and presenting a thorough review of the various types of UAV attacks, including a survey of recent real-world UAV cybersecurity incidents. UAV cybersecurity threat analysis and risk assessment methodologies are reviewed, discussing how potential attacks translate to UAV system risk. The various threat detection and countermeasure (mitigation) techniques are analyzed. Finally, this paper’s unique contribution is that it provides a survey of existing tools and datasets that are available to UAV cybersecurity researchers. A key identified research gap is the need to conduct real-world experimentation to validate proposed cybersecurity techniques. Many proposed approaches are computationally expensive or require additional redundant hardware onboard the UAV. Future research should focus on the development of lightweight methods that are practical for UAV adoption. Another key research gap is the relative lack of RemoteID cybersecurity research, despite its mandated adoption by UAVs. Lastly, this paper concludes that Global Positioning System (GPS)-related threats pose the greatest continued risk to UAVs.

Modernizing the electric power grid through advanced communications and intelligent devices has improved efficiency and reliability, but it has also expanded the cyberattack surface. One of the most consequential integrity threats is False Data Injection Attacks (FDIAs), which can compromise the measurements and data streams that underpin monitoring, protection, and control functions. This paper surveys FDIA research in smart grids and organizes attack targets into five domains: steady-state control; transient and auxiliary control; substation control; energy and load control; and AI-based systems. We review how attackers construct stealthy and data-driven FDIAs, summarize their physical and economic impacts, and discuss why many existing detection mechanisms remain vulnerable. Finally, we highlight open challenges and outline research directions toward resilient, attack-aware grid architectures.

The aviation sector relies on cooperative surveillance systems such as Automatic Dependent Surveillance-Broadcast (ADS-B) and Remote Identification (RID) to enhance safety and efficiency. However, their open, unencrypted communication protocols make them vulnerable to various cyberattacks. This survey examines the current state of knowledge on attacks, detection techniques, and countermeasures for both ADS-B and RID, addressing a gap in the literature by analyzing them side by side. It categorizes attacks, including emerging threats, reviews detection methods from traditional to modern AI-based approaches and discusses existing countermeasures. Furthermore, this paper provides a list of simulation tools and open-access datasets and identifies current research challenges and future directions.

The integration of advanced information and communication technologies into modern power grids has transformed them into complex cyber-physical systems, commonly referred to as smart grids. This evolution has enhanced grid efficiency and sustainability but has also introduced cybersecurity vulnerabilities, particularly to false data injection attacks (FDIAs). FDIAs involve the malicious manipulation of sensor data and measurements, posing a threat to the integrity of grid operations. This paper provides a comprehensive review of FDIAs in smart grids, categorizes attack strategies, analyzes their impacts on grid stability and reliability, and evaluates existing detection and mitigation strategies. We also discuss the challenges associated with defending against sophisticated FDIAs and propose future research directions to enhance the resilience of smart grids against these evolving cyber threats.