Measuring and Analyzing WiMAX Security and QoS in Testbed Experiments
Bibliographic record
Abstract
Providing strong security is necessary for any wireless access networks. The latest broadband access network implementations are based on WiMAX and LTE, since they support high data rate and mobility. The WiMAX network has well structured QoS mechanisms and security architecture to support all kinds of fixed, mobile and multihop network users. Even though the existing fixed WiMAX network has well defined security architecture, it has many security issues like rouge Base Station (BS), Denial of Service (DoS) and etc. The rouge BS issue was solved in mobile WiMAX network, but the other security issues in fixed WiMAX network and the issues related to mobility like handover latency issues still exist. Most of the existing security issues in fixed and mobile WiMAX networks are solved in the upcoming international mobile telecommunication (IMT) - Advanced WiMAX network. But there are still some security issues due to high mobility support and advanced Medium Access Control (MAC) functionalities. On the other hand, Internet service providers may use the Internet Protocol Security (IPSec) for their wireless access due to its popularity in wired network. But IPSec may affect the throughput performance, since the IPSec header in each packet consumes additional bandwidth. Little research based on real experiments has been reported comparing WiMAX standard security and IPSec. In this paper, the security supported by the standards and IPSec for fixed WiMAX network is evaluated using testbed experiments. From the experimental results and existing research efforts, the security level and QoS support of theoretical and practical security schemes are analyzed.
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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 | 0.002 | 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 | 0.001 | 0.001 |
| Open science | 0.001 | 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".