A Deep Learning-Based FDI Attack Detection Scheme for Secure Data Consolidation in Cooperative UxV Operations Using Zero Touch Networking
Bibliographic record
Abstract
Unmanned any vehicles (UxVs), including aerial (UAVs), ground (UGVs), surface (USVs), and underwater (UUVs) vehicles, are transforming smart ecosystems by enhancing infrastructure, industry, and urban environments. However, the diverse technologies involved in UxVs make standardization and efficient management in ultra-dense networks challenging. Zero Touch Network (ZTN) technology leverages artificial intelligence and software-defined networks to automate network management, addressing these complexities but also introducing vulnerabilities to cyber threats such as False Data Injection (FDI) attacks. This paper introduces a secure data consolidation scheme where UxV operation data is validated with a deep learning model before being integrated into the ZTN for seamless management. A simplified transformer-based FDI attack detection model is proposed, enhanced through data augmentation and information gain-based feature selection. An experimental environment is established to demonstrate the feasibility of the proposed scheme, and it is shown that the proposed scheme outperforms existing methods.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".