Joint Low-Rank Factor and Sparsity for Detecting Access Jamming in Massive MTC Networks
Bibliographic record
Abstract
Due to the weak security protection capabilities of the low-cost and low-complexity massive access of machine-type devices, massive machine-type communications (mMTC) networks are extremely vulnerable to the access jamming, which can affect the correctness of activity and data detection of legitimate devices and even leads to the paralysis of the mission-critical mMTC applications. This paper studies detection problem of the access jamming in the uplink of mMTC (AJ-UM), and we propose to exploit the characteristics of the joint low-rank factor and sparsity (JLFS) to detect the AJ-UM. Our detection method is motivated by the fact that the JLFS-based feature will be significantly impacted if the AJ-UM happens. We first extract the JLFS-based feature by solving a low-rank maximum likelihood factor analysis problem with sparsity constraint, and then perform the AJ-UM detection in a sequential manner. Moreover, the proposed JLFS-based method does not need to know the accurate prior information of the JLFS-based feature in the presence or absence of the AJ-UM, which can determine the AJ-UM exists as long as there is an abrupt change in the JLFS-based feature. Numerical results are finally presented to confirm the effectiveness of the proposed JLFS-based method.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".