DeepSpoofNet:用于保护无人机免受GPS伪造攻击的框架
Aziz Ur Rehman Badar1, Danish Mahmood1, Adeel Iqbal2
1Computer Science, SZABIST, Islamabad, Pakistan.
PeerJ. Computer science
|March 26, 2025
概括
这项研究引入了一种新的框架,用于检测全球定位系统 (GPS) 对无人机 (UAV) 的伪造攻击. 拟议的方法提高了检测准确度,这对于无人机安全和可靠的导航至关重要.
科学领域:
- 网络安全 网络安全
- 航空航天工程 航空航天工程
- 人工智能的人工智能
背景情况:
- 无人驾驶飞行器 (UAV) 在各个领域越来越重要,但容易受到全球定位系统 (GPS) 伪造攻击.
- GPS伪造可能会危及无人机控制,导致捕获或破坏,需要强大的检测机制.
- 现有的机器学习方法面临着诸如不平衡的数据集和在资源有限的环境中有限的准确性等挑战.
研究的目的:
- 调查最佳特征选择 (FS) 和深度学习技术,以检测无人机的GPS伪造攻击.
- 解决当前研究的局限性,包括不平衡的数据集和次优特征选择.
- 开发一个框架,提高无人机GPS伪造检测的准确性和可靠性.
主要方法:
- 实施严格的过量采样技术来处理不平衡的数据集.
- 结合了先进的特征选择方法与混合神经网络 (NN) 架构.
- 使用卷积神经网络 (CNN) 和双向长期短期记忆 (BiLSTM) 综合模型.
主要成果:
- 拟议的差异分析 (ANOVA) + CNN-BiLSTM混合模型实现了卓越的性能.
- 特殊的结果包括98.84%的精度,99.25%的准确性,99.26%的F1得分和99.69%的回忆.
- 在预测准确度,真假阳性率,F1得分和回忆方面显著改善.
结论:
- 开发的混合模型有效地检测到无人机的GPS伪造攻击,克服了现有的研究挑战.
- 该框架提供了一个强大的解决方案,用于增强无人机对复杂的网络威胁的安全性.
- 这些发现为更安全,更可靠的自主导航系统铺平了道路.
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