安全和维护隐私的入侵检测和防止在无人驾驶飞行器的互联网
Ernest Ntizikira1, Wang Lei1, Fahad Alblehai2
1School of Software, Dalian University of Technology, Dalian 116024, China.
Sensors (Basel, Switzerland)
|October 14, 2023
概括
本研究介绍了安全和保护隐私的无人机入侵检测和预防 (SP-IoUAV) 模型,以加强智能城市的安全和隐私. 该SP-IoUAV模型在检测和防止对无人机的威胁方面取得了卓越的准确性.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 物联网的物联网,就是物联网.
背景情况:
- 无人驾驶飞行器 (UAV) 对于智能城市的操作,如监视和数据收集至关重要.
- 广泛的无人机集成引发了严重的安全和隐私问题.
- 现有的安全模型很难解决无人机互联网生态系统中的独特漏洞.
研究的目的:
- 引入安全和保护隐私的无人机入侵检测和预防 (SP-IoUAV) 模型.
- 为了提高无人机操作的安全性和数据保密性.
- 为无人机的互联网开发一个强大的入侵检测和预防系统.
主要方法:
- 利用联合学习,差异隐私和安全的多方计算来保护数据.
- 集成深度神经网络,特别是CNN-LSTM,用于实时异常检测.
- 实施了实时决策机制,包括警报和自动黑名单,以及数据库访问的多因素身份验证.
主要成果:
- 在CIC-IDS2017数据集中,SP-IoUAV模型表现出了卓越的性能.
- 实现了高精度 (99.98%),精度 (99.93%),回忆 (99.92%) 和F-Score (99.92%).
- 在比较分析中表现优于FCL-SBL,RF-RSCV和RBFNNs等现有方法.
结论:
- 该SP-IoUAV模型为确保无人机操作提供了一个全面的框架.
- 在无人机系统中支持安全和保护隐私的机器学习应用程序.
- 确立了无人机在互联网上的入侵检测和预防的新基准.
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