Traction spoofing is an important component of Global Navigation Satellite System (GNSS) intermediate attacks, and the traction scheme directly determines the concealment of spoofing. However, spoofing via conventional traction strategies can be easily detected using Time of Arrival (TOA) and power detection. Based on a BPSK-modulated signal, a novel traction strategy using traction code is proposed to suppress part of the authentication signal and form an ideal correlation peak. This strategy was modeled and simulated to verify its theoretical feasibility. Effective spoofing data were generated based on the signal generation software to verify the spoofing effect with the reception of the software receiver. It can be inferred that no significant distortion occurred throughout the traction process, and the value range of the traction speed was expanded. The received results in different scenarios demonstrated that the observations’ Root-Mean-Square Error (RMSE) percentage change in the proposed strategy is significantly better than those of conventional strategies. A Ratio Test was also performed, verifying that the strategy can bypass Signal Quality Monitoring (SQM) detection. Meanwhile, the proposed strategy remained effective when the $\mathrm{C}/{\mathrm{N}}_0$ increased to 60 dBHz. In summary, the proposed strategy exhibits destructiveness, concealment, and adaptability on the battlefield.

Compared to conventional spoofing, emerging spoofing attacks pose a heightened threat to security applications within the global navigation satellite system (GNSS) due to their subtly designed signal structures. In response, a novel spoofing detection method entitled aggregated correlation residue likelihood analysis (A-CoRLiAn) is proposed in this study. Requiring only the addition of a pair of supplementary correlators, A-CoRLiAn harnesses correlation residues to formulate a likelihood metric, subsequently aggregating weighted decisions from all tracked satellites to ascertain the presence of spoofing. Evaluated under six diverse spoofing scenarios (including emerging challenges) in the Texas Spoofing Test Battery (TEXBAT) via Monte Carlo simulations, A-CoRLiAn yields a detection rate of 99.71%, demonstrating sensitivity, robustness, autonomy, and a lightweight architecture conducive to real-time implementation against spoofing threats.