Bibliographic record
Abstract
The Extensible Authentication Protocol (EAP) is widely used as an authentication framework to control the access to wireless networks, e.g. in IEEE 802.11 and IEEE 802.16 networks. In this paper, we discuss limitations of EAP security and demonstrate how these limitations can be exploited to launch attacks on existing EAP methods. In particular, we present a series of attacks which cause some standard security claims, namely channel binding, protected ciphersuite negotiation and cryptobinding, to fail and compromise the key exchange, authentication and privacy of EAP communications. Next, we identify the special security challenges of EAP systems that may cause the considered security claims to fail. EAP differs from other authentication frameworks as a two party protocol, like IKE and TLS, because it is conducted with three parties involved across two communication links with different media. Another security challenge of EAP is the negotiability of EAP methods, ciphersuites, and protocol versions. These challenges make it difficult to derive a trust model for EAP and to securely adopt existing protocols. Finally, we conclude with recommendations for more secure EAP implementations.
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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.006 |
| Scholarly communication | 0.011 | 0.016 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".