利用机器学习来检测全球导航卫星系统中的结构性干扰
Imtiaz Nabi1, Salma Zainab Farooq1,2, Sunnyaha Saeed1
1National Center of GIS and Space Applications (NCGSA), Institute of Space Technology, Islamabad, Pakistan.
PeerJ. Computer science
|December 9, 2024
概括
机器学习有效地检测出复杂的全球导航卫星系统 (GNSS) 伪造攻击. 后勤回归模型为识别假冒信号提供了卓越的时间效率和准确性,这对航空安全至关重要.
科学领域:
- 导航系统 导航系统
- 信号处理 信号处理
- 机器学习 机器学习
背景情况:
- 无线电频率干扰,特别是全球导航卫星系统 (GNSS) 伪造,对导航和定时服务构成重大威胁.
- 复杂的伪造攻击,如安全代码估计和重复 (SCER) 攻击,挑战现有的检测和缓解技术.
- 准确的伪造识别对于生命安全应用至关重要,特别是在航空领域,那里的接收器可能无法检测到假冒信号.
研究的目的:
- 探索机器学习 (ML) 技术在区分真实GNSS信号和伪造信号方面的有效性.
- 为了特别解决具有挑战性的SCER伪造攻击,使用德克萨斯伪造测试电池 (TEXBAT) 数据集.
- 评估和比较后勤回归,支持向量机 (SVM),K-近邻 (KNN) 和决策树模型的性能.
主要方法:
- 利用来自延迟锁循环相关器的跟踪数据作为ML模型训练的内在特征.
- 训练了四种不同的ML模型:后勤回归,SVM,KNN和决策树.
- 采用随机六倍交叉验证方法来进行模型培训和评估.
主要成果:
- 后勤回归和SVM都在检测伪造时获得了94%的F1平均得分.
- 后勤回归证明了显著的时间效率优势,超过SVM165dB和决策树3倍.
- 接收器操作特征 (ROC) 曲线分析进一步支持评估模型的性能.
结论:
- 逻辑回归被认为是识别SCER结构干扰的最理想方法,因为它的高精度和优越的时间效率.
- 机器学习,特别是物流回归,提供了一个强大的解决方案,用于加强GNSS安全,防止先进的伪造威胁.
- 这些发现有助于开发更具弹性导航系统,特别是在关键的航空应用中.
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