Throughput and Stability Improvements of Slotted ALOHA Based Wireless Networks under the Random Packet Destruction Dos Attack
Bibliographic record
Abstract
A random packet destruction Denial of Service (DoS) attacking signals are easy to mount and difficult to detect and prevent. The attacker does not need to pretend a legal user and able to shut down any Slotted ALOHA based wireless Ad Hoc and sensor networks successfully by reducing the throughput and stability. Since current anti-attack measures such as encryption, authentication and authorization cannot prevent these types of attacks, we propose the use of multiple power levels transmission system to mitigate the attacking signals. Through analysis and numerical examples we demonstrate that the multiple power levels transmission system can significantly improve the throughput and stability of Slotted ALOHA under the random packet destruction Denial of Service (DoS) attacking signals. The implementation of the multiple power levels transmission system is easy and can keep the system running, although current anti-attack measures such as encryption, authentication and authorization cannot prevent these types of attacks.
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How this classification was reachedexpand
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 | 0.001 | 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".