Preventing unauthorized access in information centric networking
Bibliographic record
Abstract
The increasing traffic volume and new requirements of highly scalable and efficient distribution of contents exceed the capabilities of the current Internet architecture. Information centric networking (ICN) is a new communication paradigm for the next generation internet (NGI), which focuses mainly on contents. ICN has in‐network caching capability, which enables any node to cache any content coming from any publisher. ICN subscribers are able to access contents from different distributed locations. This capability maximizes the problem of unauthorized access to ICN contents. In this paper, we propose a decentralized elliptic curve‐based access control (ECAC) protocol for ICN architectures. In this protocol, fewer public messages are needed for access control enforcement between ICN subscribers and ICN nodes than the existing access control protocols. ECAC protocol depends on ICN self‐certifying naming scheme. We perform security analysis on ECAC for the following attacks: man‐in‐the‐middle, forward security, replay attacks, integrity, and privacy violations. We also evaluate communication, computational, and storage overhead for performance analysis to ECAC. Based on our results that are obtained under various scenarios, ECAC efficiently prevents unauthorized access to ICN contents.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| 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".