组装机器学习模型使用混合递归特征消除 (RFE) 技术来检测针对无人机的GPS伪造攻击
Raghad Al-Syouf1, Omar Y Aljarrah1, Raed Bani-Hani1
1Department of Network Engineering and Security, Jordan University of Science and Technology, Irbid 22110, Jordan.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
无人驾驶飞行器 (UAV) 面临GPS伪造威胁. 一种新的混合递归特征消除 (RFE) 方法与集体学习提高了无人机入侵检测效率和准确性.
科学领域:
- 网络安全 网络安全
- 航空航天工程 航空航天工程
- 机器学习 机器学习
背景情况:
- 无人机依赖于外部数据,因此容易受到GPS干扰和欺骗攻击.
- 侵入检测系统 (IDS) 对无人机安全至关重要,机器学习 (ML) 为网络威胁提供高检测率.
- 无人机中现有的基于ML的IDS面临着计算效率和处理时间的挑战.
研究的目的:
- 提出一种混合递归特征消除 (RFE) 技术与斯皮尔曼相关性分析 (SCA) 结合,用于在无人机中高效地检测GPS伪造.
- 用拟议的特征选择方法评估集合学习分类器 (包装,提升,堆叠,投票) 的性能.
- 提高无人机入侵检测系统的准确性和计算效率.
主要方法:
- 开发了一种混合特征选择方法,该方法结合了递归特征消除 (RFE) 和斯皮尔曼相关性分析 (SCA).
- 集体学习模型,包括包装,提升,堆叠和投票分类器,用于GPS伪造检测.
- 拟议的方法在两个基准数据集上进行了评估:GPS伪造数据集和无人机位置GPS伪造数据集.
主要成果:
- 拟议的组合模型显示了检测准确性和处理效率之间的强烈平衡.
- 包装分类器在低处理时间 (0.029秒) 的GPS伪造数据集中实现了最高准确率 (99.50%).
- 在无人机位置GPS伪造数据集上,包装分类器实现了99.16%的准确性和0.002秒的处理时间,优于其他ML模型.
结论:
- 基于混合RFE的特征选择方法显著提高了无人机GPS伪造检测的效率和有效性.
- 集体学习分类器,特别是包装分类器,在无人机入侵检测的准确性和速度方面表现出色.
- 拟议的方法在检测无人机的GPS伪造攻击方面优于传统的特征选择技术.
关键词:
在GPS中伪造GPS伪造.无人机无人驾驶飞行器 (UAV) 是一种无人机.网络攻击,网络攻击.组合模型组合模型组合模型侵入检测系统 (IDS) 是一种入侵检测系统.机器学习 (ML) 是指机器学习.更多相关视频
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