Secure machine‐type communications in LTE networks
Bibliographic record
Abstract
Abstract With a great variety of potential applications, machine‐type communications (MTC) is gaining a tremendous interest from mobile network operators and research groups. MTC is standardized by the 3rd Generation Partnership Project (3GPP), which has been regarded as the promising solution facilitating machine‐to‐machine communications. In the latest standard, 3GPP proposes a novel architecture for MTC, in which the MTC server is located outside the operator domain. However, the connection between the 3GPP core network and MTC server in this scenario is insecure; consequently, there are distrustful relationships among MTC device, core network, and MTC server. If the security issue is not well addressed, all applications involved in MTC cannot be put into the market. To address this problem, we propose an end‐to‐end security scheme for MTC based on the proxy‐signature technique, called E2SEC. Specifically, both the MTC device and MTC server can establish strong trustful relationships with each other by using the proxy signatures issued by the 3GPP core network. Moreover, we present some implementation considerations of E2SEC and analyze the performance during authentication by comparing the operational cost of three cases that apply three different signature algorithms, that is, ElGamal, Schnorr, and DSA. Through security analysis by using Automatic Cryptographic Protocol Verifier (ProVerif), we conclude that the proposed E2SEC scheme can achieve the security goals and prevent various security threats. Copyright © 2015 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".