## 4 Drone Forensics and Security Solutions--Review
A further review of drone-assisted public safety networks is shared by [PERSON] et al. [138]. **Machine learning for threat detection**: [PERSON] et al. [139] in 2023, [PERSON] et al. [140], and [PERSON] et al. [141], alongside many scholars, studied various machine learning algorithms, such as the random forest classifier, to be implemented to detect network attacks, including DoS, port scanning, and brute force attempts. This predictive analytics approach analyzed network traffic patterns to identify anomalies indicative of security breaches. It targeted UAVs (Z1), communication systems (Z2), control hubs (Z3), command centers (Z4), and regulatory bodies (Z5). The solution enhanced confidentiality (C), integrity (I), and availability (A) by providing the early detection and mitigation of threats. A comprehensive review was provided by [PERSON] et al. [142] in 2023. **Blockchain for UAV signal security**: The work of [PERSON] et al. [143] in 2023, [PERSON] et al. [144], [PERSON] et al. [145], and [PERSON] et al. [146] applied blockchain technology to secure the signal transmission between controllers and UAVs. Their methods secured the communication protocol, ensuring that signals were authentic and had not been tampered with. The primary targets were UAVs (Z1), communication systems (Z2), control hubs (Z3), and command centers (Z4). While enhancing confidentiality (C) and integrity (I), the approach required robust mechanisms to ensure signal availability (A). **Software-defined networks (SDNs) for drone swarms**: The works of [PERSON] et al. [147] in 2024, [PERSON] et al. [141] considered SDN solutions to manage and coordinate drone swarms, facilitating centralized control and dynamic responses to security threats. Their architectures separated the control plane from the data plane, allowing for more flexible and responsive security management. Their target areas included UAVs (Z1), communication systems (Z2), and control hubs (Z3). An SDN enhances confidentiality (C) and integrity (I), but continuous monitoring is necessary to maintain availability (A) against coordinated attacks. **Light-weight hardware security**: [PERSON] et al. [148] in 2024 and [149] in 2023 considered light-weight hardware solutions to ensure the confidentiality and integrity of command data and payload data. These hardware-based security measures were integrated directly into the drone's components, providing a tamper-resistant environment. The primary targets were UAVs (Z1), communication systems (Z2), and control hubs (Z3). These solutions significantly enhanced confidentiality (C) and integrity (I) but may not fully address availability (A) concerns. **Multi-sensor detection systems**: The work of [PERSON] et al [150] considered multi-sensor detection systems using various sensors to detect unauthorized drone activity in restricted areas. This approach enhances detection accuracy by cross-referencing data from multiple sources. The key targets were communication systems (Z2), control hubs (Z3), and regulatory bodies (Z5). These systems improve integrity (I) and availability (A) by ensuring timely detection and response to intrusions. Table 3 lists recent drone security solutions and their associated defense mechanisms. The current landscape of drone security solutions leverages advanced cryptographic techniques, blockchain technology, machine learning, software-defined networking, and multi-sensor systems to address the diverse and evolving threats facing UAV operations. While these solutions collectively enhance the confidentiality, integrity, and availability of drone systems, ongoing research and development are essential to adapt to new security challenges and ensure robust protection for UAVs in various applications.