Abstract: Signal quality monitoring (SQM) is effective in detecting spoofing attacks. However, traditional SQM methods struggle with complex spoofing strategies due to their simple structures, resulting in reduced detection accuracy and reliability. Induction-based spoofing attacks cause consistent variation trends in induction parameters across satellite tracking channels, leading to high similarity in inter-channel SQM metrics. Consequently, spoofing can be detected by analyzing these inter-channel correlations. However, in practical environments, the induction processes across different channels are not strictly synchronized, which degrades the correlation when calculated directly. To address this, a detection method based on dynamic time warping (DTW) is proposed to quantify the similarity between SQM sequences effectively, thereby enhancing detection robustness. The proposed metric was validated using the Texas Spoofing Test Battery (TEXBAT) dataset and evaluated against traditional SQM processing algorithms. Experimental results demonstrate that the proposed algorithm significantly improves both detection efficiency and reliability across various spoofing scenarios.