A self‐stabilized random access protocol against denial of service attack in wireless networks
Bibliographic record
Abstract
Abstract One of the main drawbacks of Slotted ALOHA is its throughput collapse at higher traffic load condition due to excessive collisions and known as stability problem. A random packet destruction Denial of Service (DoS) attack can increase the throughput collapse by increasing the collisions further. The current security protection techniques such as encryption, authentication and authorization cannot prevent these types of attacks, since the attacking packets destroy those packets by colliding those encrypted, authenticated and authorized packets. The maximum throughput of Slotted ALOHA can be achieved by the knowledge of the number of active mobile nodes and the average rate of the attacking packet arrival rate. However, the knowledge of these two parameters' current values are difficult and sometimes impossible to know. A self‐stabilized Slotted ALOHA system against the random packet destruction attacking noise packets is presented in this paper. Results show that the system provides nearly optimal stable throughput without the knowledge of current active number of mobile nodes and current attacking packets arrival rate. The proposed system is truly distributive in nature and can be easily implemented in wireless access systems without requiring any centralized control and can defend against random packet destruction DoS attack. Copyright © 2010 John Wiley & Sons, Ltd.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".