Enhanced User Security and Privacy Protection in 4G LTE Network
Bibliographic record
Abstract
Although the Evolved Packet System Authentication and Key Agreement (EPS-AKA) provides security and privacy enhancements in 3rd Generation Partnership Project (3GPP), the International Mobile Subscriber Identity (IMSI) is sent in clear text in order to obtain service. Various efforts to provide security mechanisms to protect this unique private identity have not resulted in methods implemented to protect the disclosure of the IMSI. The exposure of the IMSI brings risk to user privacy, and knowledge of it can lead to several passive and active attacks targeted at specific IMSI's and their respective users. Further, the Temporary Mobile Subscribers Identity (TMSI) generated by the Authentication Center (AuC) have been found to be prone to rainbow and brute force attacks, hence an attacker who gets hold of the TMSI can be able to perform social engineering in tracing the TMSI to the corresponding IMSI of a User Equipment (UE). This paper proposes a change to the EPS-AKA authentication process in 4G Long Term Evolution (LTE) Network by including the use of Public Key Infrastructure (PKI). The change would result in the IMSI never being released in the clear in an untrusted network.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| 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".