Secured operating regions of Slotted ALOHA in the presence of interfering signals from other networks and DoS attacking signals
Bibliographic record
Abstract
It is expected that many networks will be providing services at a time in near future and those will also produce different interfering signals for the current Slotted ALOHA based systems. A random packet destruction Denial of Service (DoS) attacking signal can shut down the Slotted ALOHA based networks easily. Therefore, to keep up the services of Slotted ALOHA based systems by enhancing the secured operating regions in the presence of the interfering signals from other wireless systems and DoS attacking signals is an important issue and is investigated in this paper. We have presented four different techniques for secured operating regions enhancements of Slotted ALOHA protocol. Results show that the interfering signals from other wireless systems and the DoS attacking signals can produce similar detrimental effect on Slotted ALOHA. However, the most detrimental effect can be produced, if an artificial DoS attack can be launched using extra false packets arrival from the original network. All four proposed secured operating regions enhancement techniques are easy to implement and have the ability to prevent the shutdown of the Slotted ALOHA based networks.
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 | 0.000 | 0.001 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".