CTDNN-Spoof:一个紧的微小深度学习架构,用于在小型无人机中检测和多标签分类GPS伪造攻击
Ahmad Almadhor1, Jamel Baili2, Shtwai Alsubai3
1Department of Computer Engineering and Networks, College of Computer and Information Sciences, Jouf University, Sakaka, 72388, Saudi Arabia.
Scientific reports
|February 24, 2025
概括
一个新的紧型深度学习模型,CTDNN-Spoof,有效地检测和分类对小型无人机 (UAV) 的GPS伪造攻击. 这提高了UAV在关键应用中的安全性和可靠性.
科学领域:
- 网络安全 网络安全
- 人工智能的人工智能
- 航空航天工程 航空航天工程
背景情况:
- GPS伪造对小型无人飞行器 (UAV) 构成重大威胁,危害导航系统,并导致安全风险,隐私侵犯和任务失败.
- 在监视,农业和环境监测中,小型无人机的可靠运行需要强大的防御系统来防止GPS伪造.
研究的目的:
- 提出一种新的,紧的深度学习架构,CTDNN-Spoof,用于实时检测和多标签分类针对小型无人机的GPS伪造攻击.
- 评估CTDNN-Spoof模型与现有方法的性能,复杂性和适应性.
主要方法:
- 开发一个紧的,微小的深度学习架构 (CTDNN-Spoof),具有具有特定层配置和ReLU/线性激活的顺序神经网络.
- 使用亚当优化器进行优化,并进行平均平方误差 (MSE) 损失和准确性评估,包括早期停止和50个时代的训练.
- 与其他两个模型进行比较分析,以评估复杂性,损失和准确性.
主要成果:
- CTDNN-Spoof模型在不同的伪造攻击标签中显示出不同的精度.
- 与基线模型相比,拟议的架构实现了卓越的性能和有利的时间复杂性.
- 该模型在缓解GPS伪造威胁方面被证明是有效的,提供了一个可扩展的实时解决方案.
结论:
- CTDNN-Spoof提出了一种有效和创新的方法,以提高小型无人机对GPS伪造攻击的安全性.
- 该模型在精度和适应性方面超越了传统方法,确保了更可靠的无人机操作.
- 这项研究在确保无人机导航系统安全方面取得了重大进展.
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