In smart cities, unmanned aerial vehicles (UAVS) play a vital role in surveillance, monitoring, and data collection. However, the widespread integration of UAVs brings forth a pressing concern: security and privacy vulnerabilities. This study introduces the SP-IoUAV (Secure and Privacy Preserving Intrusion Detection and Prevention for UAVS) model, tailored specifically for the Internet of UAVs ecosystem. The challenge lies in safeguarding UAV operations and ensuring data confidentiality. Our model employs cutting-edge techniques, including federated learning, differential privacy, and secure multi-party computation. These fortify data confidentiality and enhance intrusion detection accuracy. Central to our approach is the integration of deep neural networks (DNNs) like the convolutional neural network-long short-term memory (CNN-LSTM) network, enabling real-time anomaly detection and precise threat identification. This empowers UAVs to make immediate decisions in dynamic environments. To proactively counteract security breaches, we have implemented a real-time decision mechanism triggering alerts and initiating automatic blacklisting. Furthermore, multi-factor authentication (MFA) strengthens access security for the intrusion detection system (IDS) database. The SP-IoUAV model not only establishes a comprehensive machine framework for safeguarding UAV operations but also advocates for secure and privacy-preserving machine learning in UAVS. Our model’s effectiveness is validated using the CIC-IDS2017 dataset, and the comparative analysis showcases its superiority over previous approaches like FCL-SBL, RF-RSCV, and RBFNNs, boasting exceptional levels of accuracy (99.98%), precision (99.93%), recall (99.92%), and F-score (99.92%).

Future communication paradigms, such as 6G networks, emphasize self-sustainability, intelligent networking, and secure, adaptive communication. This research presents an innovative routing framework tailored for Underwater Sensor Networks (UWSNs) and Underwater Acoustic Networks (UANs), addressing critical challenges like energy constraints, security vulnerabilities, limited bandwidth, and interference. The proposed system integrates a Multi-Agent System (MAS), blockchain technology, and acoustic communication to enhance security, optimize energy usage, and improve data transmission efficiency. Key features include intelligent node mechanisms, proactive bandwidth and interference management, a multi-hop paradigm, distance-aware longevity strategies, and robust cryptographic protocols. The system is benchmarked against established routing protocols such as GCORP, PER, MARL-MC, and MLAR, demonstrating superior performance. The proposed cognitive intelligence (CI) protocol achieves energy consumption below 120 J per transmission, significantly lower than existing methods. It also achieves end-to-end latency under two seconds in multi-hop scenarios, outperforming alternatives like MARL-MC and GCORP. Additionally, the CI protocol exhibits a packet delivery ratio (PDR) exceeding 90% and an extended network lifetime surpassing 1850 s, making it a robust solution for resource-constrained underwater environments. This work not only addresses the unique demands of underwater networks but also contributes to the vision of self-sustainable and intelligent communication systems, aligning with the broader context of 6G paradigms through energy-efficient routing, cognitive intelligence, and secure, adaptive communication frameworks. The results underscore the effectiveness of the CI protocol in enhancing energy efficiency, reducing latency, and ensuring reliable long-term operation, thereby supporting critical applications like disaster management and environmental monitoring.

Wireless networks that can exchange any type of data are vulnerable to multiple intrusions and increase potential security risks, so the design of an Intrusion Detection and Prevention System (IDPS) that analyzes the packet features and detects different intruders (i.e., the types of attack) is necessary. Whereas, the existence of redundant and irrelevant features hinders the potential of IDPS. In this paper, we propose TL-IDPS, a Two-Level classification IDPS of wireless network based on optimized features. In the phase of intrusion detection, one-hot method, normalization and correlation estimation are used to mitigate the redundant features. Then, the fuzzy membership function with cuttlefish algorithm maps and consolidates the extracted features and selects optimal features. Based on the optimal features, Di-distance k-nearest neighbor (K-NN) as the first level classify the intruder or non-intruder. Further the type of intruder is identified by deep Q-network. From the result of detected intruders, the further arrival of those intruders is prevented. Experimental results conducted from multiple evaluation metrics using the UNSW-NB15 dataset prove that our proposed TL-IDPS is more effective than existing IDPS methods.