Bibliographic record
Abstract
The IEEE 802.11 wireless LAN standard was established in 1989 and was originally intended to seek a wireless equivalent to Ethernet. In this way, it has developed successions of robust enterprise grade solutions that in many cases meet or even exceed the demands of the enterprise network. IEEE 802.11 wireless LAN networks are designed to provide wireless connectivity in a range of roughly 300 feet from the base. The lead application being shared over the wireless LAN is data. Provisions are being made to accommodate audio, video, and other forms of streaming multimedia. Wireless security is a major demand in the secure data transferring services. Security challenges such as identity theft, international credit card fraud, communications fraud and corporate fraud are some of the main barriers preventing wireless technologies from growing and over taking the wired technology position. The 802.11b wireless LAN includes a protocol called wired equivalent privacy (WEP) which is meant to protect the wireless network. We have been able to find some major flaws in this protocol which lead the whole system to be insecure and thus unreliable. For example, the cryptographic technique used in the WEP protocol, which is a fundamental tool to achieve the above goals, is rather primitive and can be broken fairly simply. The purpose of this paper is to explore the security vulnerabilities of the 802.11b wireless LAN and to present solutions for some of its major vulnerabilities.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".