# Comprehensive Review of UAV Detection, Security, and Communication Advancements to Prevent Threats
## 8 Current State-of-the-Art Solutions  
The very first and significant thing to resolve the threats is to identify them first. Thus, Table 5 and Figure 10 classify these attacks. Moreover, there are several contributions which address these sorts of attacks along with the suitable measures [58]. In recent times, researchers have utilized machine learning approaches to demonstrate the intrusion detection system (IDS). Thus, machine learning (ML)-based IDS is one of the areas where researchers are still working to improve the results [59; 60]. Blockchain is also among the most effective approaches for UAV/drone security and privacy [12]. This ML-based IDS technique is from the robust technique, and it is categorized into three kinds as mentioned below:  
* Rule-based IDS;
* Signature-based IDS;
* Anomaly-based IDS. Above are the major approaches for detecting threats that intrusion detection systems utilize to inform the operator in the ground control room (GCR). Rule-based threat detection is a new approach enabled by artificial intelligence (AI) [61]. In comparison to others, it is more reliant on technology and less on manual interaction. Signature-based detection works well for recognizing known threats. It uses a pre-programmed list of known threats and their indicators of compromise to operate (IOCs). An indicator of compromise (IOC) could be a distinctive behavior that typically precedes a malicious network attack, such  
Figure 10: UAV attack vector with reported incidents. as file hashes, malicious sites, known byte sequences, or even the content of email subject headings. A signature-based IDS examines network packets and compares them to a database of known IOCs or attack signatures to detect any suspicious behavior. Anomaly-based intrusion detection systems, on the other hand, can alert you to unusual behavior. An anomaly-based detection system uses machine learning to educate the detection system to recognize a normalized baseline rather than searching for known threats. All network activity is compared to the baseline, which represents how the system ordinarily performs. Rather than looking for known IOCs, anomaly-based IDS simply detects any unusual behavior to generate alarms. To identify the false data injection attacks, one may use the rule-based approach. This is used to target the signal strength in between the UAV and ground control room (GCR) and can be useful for any sort of known attack, pattern, or technique only. Some research papers have proposed a signature-based IDS over drones [62] where they addressed bio-inspired cyberattacks associated with air-born networks. Last, but certainly not least, is the anomaly-based IDS scheme which is used against jamming attacks [62]. The only limitation of anomaly IDS is the huge resource requirement. Similarly, there are some researchers who have suggested some algorithm schemes with forensic methods to address the advanced and complex attacks. They are complex and difficult to identify [63; 64; 65; 66; 67; 68; 69]. With the help of forensics, both perpetrator and method of attack can be identified. With the identification of the attack type, appropriate countermeasures can be implemented to avoid any future incident. As per the survey of [70], between 2014 and 2017 incidents among airplanes and drones amplified from 6 to 93, which makes it very important for the authorities to address security and privacy issues for UAVs. Due to an increase in cyberattacks on drones/UAVs, the government needs to introduce strict policies and standards to minimize these concerns. With the popularity of UAVs among the civilian population, attacks and the illegal use of UAVs will likely proliferate. Civilian or domestic UAV countermeasures are divided into physical and local countermeasures, which are already proposed but still can be improved.