Global Navigation Satellite Systems (GNSS) are integrated into many devices. However, civilian GNSS signals are usually not cryptographically protected. This makes attacks that forge signals relatively easy. Considering modern devices often have network connections and onboard sensors, the proposed here Probabilistic Detection of GNSS Spoofing (PDS) scheme is based on such opportunistic information. PDS has at its core two parts. First, a regression problem with motion model constraints, which equalizes the noise of all locations considering the motion model of the device. Second, a Gaussian process, that analyzes statistical properties of location data to construct uncertainty. Then, a likelihood function, that fuses the two parts, as a basis for a Neyman-Pearson lemma (NPL)-based detection strategy. Our experimental evaluation shows a performance gain over the state-of-the-art, in terms of attack detection effectiveness.

翻译：全球导航卫星系统(GNSS)已集成至众多设备之中，然而民用GNSS信号通常缺乏加密保护，这使得信号伪造攻击相对容易实施。鉴于现代设备普遍具备网络连接和机载传感器能力，本文提出的GNSS欺骗概率检测(PDS)方案正是基于此类机会信息。PDS的核心包含两部分：首先，构建具有运动模型约束的回归问题，通过设备运动模型均衡所有位置噪声；其次，采用高斯过程分析位置数据的统计特性以构建不确定性度量。随后设计似然函数融合上述两部分，并基于奈曼-皮尔逊引理(NPL)构建检测策略。实验评估表明，本方法在攻击检测效能方面相较于现有最优方法具有显著性能提升。