基于深度学习的位置伪造攻击检测和通过物联网网络中接收的功率来估计到达时间
1Department of Computer Engineering, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 11942, Saudi Arabia.
Sensors (Basel, Switzerland)
|December 9, 2023
概括
本研究介绍了一个神经网络模型,用于在物联网 (IoT) 中检测和定位伪造的无人机 (UAV). 该系统使用单一访问点 (AP) 检测来应对位置伪造威胁.
科学领域:
- 网络安全和网络工程 网络安全和网络工程
- 人工智能和机器学习
- 物联网 (IoT) 安全 安全 物联网
背景情况:
- 物联网 (IoT) 中的基于位置的应用程序容易受到位置欺骗攻击.
- 恶意假冒者可以冒充设备,破坏无线通道,并误导物联网节点,影响位置准确性.
- 现有的安全措施很难有效地检测和减轻复杂的伪造技术.
研究的目的:
- 开发一个强大的基于神经网络的模型,用于检测和定位伪造的无人机 (UAV).
- 解决物联网环境中使用单一接入点 (AP) 检测能力的位置伪造的挑战.
- 为了估计伪造信号的到达时间 (ToA),以提高定位准确度.
主要方法:
- 一种新的特征提取技术,利用单个访问点 (AP) 进行伪造信号检测.
- 实施神经网络模型 (MLP和LSTM) 来检测伪造的无人机存在并估计ToA.
- 一种集中式的方法,用于有效收集数据和定位伪造设备.
主要成果:
- 拟议的神经网络模型在检测伪造无人机方面表现出有效性.
- 实现了对伪造信号的到达时间 (ToA) 的准确估计.
- 多层感知器 (MLP) 和长短期记忆 (LSTM) 模型之间的比较突出了它们在检测和ToA预测方面的表现.
结论:
- 开发的神经网络模型为打击物联网中的位置伪造提供了一个有希望的解决方案.
- 单个AP检测与先进的机器学习技术相结合,提高了基于位置的物联网服务的安全性.
- 这些发现有助于保护物联网网络免受针对设备位置的恶意攻击.
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