Bibliographic record
Abstract
In this paper, we concentrate on the security issues of the integrity protection of LTE and present two different forgery attacks. For the first attack, referred to as a linear forgery attack, EIA1 and EIA3, two integrity protection algorithms of LTE, are insecure if the initial value (IV) can be repeated twice during the life cycle of an integrity key (IK). Because of the linearity of EIA1 and EIA3, given two valid Message Authentication Codes (MACs) our algorithm can forge up to 2 32 valid MACs. Thus, the probability of finding a valid MAC is dramatically increased. Although the combination of IV and IK never repeats in the ordinary case, in our well-designed scenario, the attacker can make the same combination occur twice. The duplication provides the opportunity to conduct our linear forgery attack, which may harm the security of communication. To test our linear forgery attack algorithm, we generate two counter check messages and successfully forge the third one. We also examine the attack timing by simulating real communication. From the experimental results, our attack is applicable. The second attack is referred to as a trace extension forgery attack, which works only in theory. However, this attack is more general than the linear forgery attack. Known only one MAC and message pair, we can construct a different message, who has the same MAC as the original one, with the probability 1 2 16. In this attack, trace function is applied to the message to shrink the guessing space. Index Terms. Forgery, MAC, LTE, man-in-the-middle. 1
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".