Spoofer-to-Target Association in Multi-Spoofer Multi-Target Scenario for Stealthy GPS Spoofing
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Global navigation satellite system (GNSS) based navigation is omnipresent in today's world, providing position, velocity, and time (PVT) information with inexpensive GPS receivers. These receivers are highly vulnerable to intentional interference like GPS spoofing and meaconing. The spoofing of a single GPS receiver using a spoofer setup is widespread, and the concept of spoofing multiple targets with multiple distributed spoofers is also equally adaptable. Traditionally, in distributed spoofers, the multiple spoofers in the surveillance region work independently without knowing other spoofers being installed. Multiple spoofers deployment and its management are optimal for misguiding the multiple GPS receivers in the given surveillance. This paper presents a generalized mathematical model for the multi-spoofer multi-target (MSMT) scenario, spoofer management, and spoofer-to-target association. The received power of spoofed signals is considered as an evaluating parameter for locking the spoofed signals onto the GPS receivers. Three novel centralized networking-based spoofing techniques are proposed to overcome spoofer-to-target association in distributed networking. Firstly, the global nearest neighbor (GNN) based centralized spoofing is proposed. The overall cost of the function is minimized by assigning a unique spoofer-ID to a unique target-ID. In GNN-based centralized spoofing, the overall global cost minimizes, but it does not ensure that every target-to-spoofer assignment is minimum. Secondly, the spoofers of opportunity-based centralized spoofing with the GNN association is proposed to resolve the spoofer-to-target association and to increase the hit ratio. However, it is hard to install more spoofers; therefore, a tunable transmitting power-based centralized spoofing with the GNN association is presented to accomplish efficient spoofer-to-target association and higher hit-ratio. The spoofing efficiency is evaluated using spoofer-to-target association, hit ratio, and position root mean square error (PRMSE). All the proposed algorithms outperform the distributed spoofing. We also observe that the tunable power-based spoofing is an optimal solution in MSMT scenario.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it