Ntizikira Ernest, Lei Wang, Alblehai Fahad, Saleem Kiran, Lodhi Muhammad Ali
School of Software, Dalian University of Technology, Dalian 116024, China.
Department of Computer Science, Community College, King Saud University, Riyadh 11437, Saudi Arabia.
Sensors (Basel). 2023 Sep 25;23(19):8077. doi: 10.3390/s23198077.
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 -Score (99.92%).
在智慧城市中，无人机在监视、监测和数据收集方面发挥着至关重要的作用。然而，无人机的广泛集成引发了一个紧迫的问题：安全和隐私漏洞。本研究介绍了专门为无人机物联网生态系统量身定制的SP-IoUAV（无人机安全与隐私保护入侵检测与预防）模型。挑战在于保障无人机操作并确保数据机密性。我们的模型采用了前沿技术，包括联邦学习、差分隐私和安全多方计算。这些技术强化了数据机密性并提高了入侵检测准确性。我们方法的核心是集成深度神经网络（DNN），如卷积神经网络-长短期记忆（CNN-LSTM）网络，能够进行实时异常检测和精确的威胁识别。这使无人机能够在动态环境中立即做出决策。为了积极应对安全漏洞，我们实施了一种实时决策机制，触发警报并启动自动黑名单。此外，多因素认证（MFA）加强了入侵检测系统（IDS）数据库的访问安全性。SP-IoUAV模型不仅为保障无人机操作建立了一个全面的机器框架，还倡导在无人机中进行安全和隐私保护的机器学习。我们模型的有效性通过CIC-IDS2017数据集得到验证，对比分析表明其优于FCL-SBL、RF-RSCV和RBFNN等先前方法，具有极高的准确率（99.98%）、精确率（99.93%）、召回率（99.92%）和F1分数（99.92%）。
