Federated learning offers a robust framework for enhancing the security of underwater drones by allowing them to collaboratively learn from localized data without sharing sensitive information. This decentralized approach minimizes the risk of data breaches, as drone units can develop models based on their environmental data while only exchanging model updates instead of raw data. By leveraging federated learning, underwater drones can improve their real-time autonomous navigation, target recognition, and anomaly detection capabilities, adapting to dynamic underwater conditions while maintaining data privacy and resilience against cyber threats.
Federated learning presents a powerful method for enhancing underwater drones’ security, efficiency, and responsiveness. By fostering a collaborative learning environment with a focus on data privacy, these systems can better adapt to their operational environments, making them safer and more effective for a variety of applications. Continued research and innovation in federated learning techniques will further enhance the capabilities of these underwater systems while safeguarding their sensitive data.

 

 

 

The Internet of Underwater Things (IoUT) is an emerging paradigm that enhances underwater exploration and monitoring through the integration of various sensors and devices. However, this interconnectedness makes the IoUT susceptible to various cyber threats, including Distributed Denial of Service (DDoS) attacks. Detecting DDoS attacks in IoUT is critical to ensuring the integrity and availability of communication channels among underwater devices.
Detecting DDoS attacks in the IoUT using Packet Capture (PCAP) analysis involves monitoring and analyzing network traffic patterns for anomalies that may indicate potential threats. By capturing packets transmitted between underwater devices and processing this data with intrusion detection systems (IDS), researchers can identify unusual spikes in traffic volume, irregular packet flows, and specific signatures associated with DDoS attack methodologies. Techniques such as identifying the source of traffic, analyzing flow statistics, and employing machine learning algorithms can enhance the accuracy of detection. Moreover, incorporating real-time monitoring and response mechanisms is crucial for mitigating the impact of such attacks on critical underwater communication systems.
Detecting DDoS attacks in the IoUT through PCAP analysis requires a multi-faceted approach, leveraging network traffic patterns, flow statistics, and machine learning models to identify anomalies swiftly. Real-time monitoring and adaptive mitigation strategies are vital to protect critical underwater communication systems from increasing cyber threats.

 

 

 

In the rapidly evolving Internet of Things (IoT) ecosystem, ensuring secure communication among resource-constrained devices is of paramount importance. An ECC-based Authentication and Key Agreement Protocol was designed to provide a secure, efficient method for device authentication and creation of shared keys using Elliptic Curve Cryptography (ECC). This approach maximizes security while minimizing computational requirements, making it ideal for IoT applications.
An ECC-based Authentication and Key Agreement protocol for IoT leverages Elliptic Curve Cryptography to provide robust security with minimal computational overhead, making it ideal for resource-constrained devices. The protocol typically involves a lightweight mechanism where devices authenticate each other using public keys while establishing a shared secret for encrypted communication. By utilizing ECC, the protocol achieves high-security levels with shorter keys compared to traditional methods, thus enhancing efficiency in both processing and power consumption. This approach effectively mitigates threats such as eavesdropping and replay attacks while ensuring scalability in diverse IoT environments.