Context-Aware Security Policy Agent for Mobile Internet Services
Bibliographic record
Abstract
The recent proliferation of e-services on the Internet (e.g. e-commerce, e-health) and the increasing attacks on them by malicious individuals have highlighted the need for e-service security. E-services on the mobile Internet (mi-services) are no exception. However, for mi-services, the level and type of security may depend on the user’s security preferences for the service, the power of the mobile platform, and the location of the mobile platform (we label these UPL). For example, if the user is traveling through a particularly dangerous area known for previous attacks, the security protection should be adjusted to use mechanisms that are resilient to these attacks. We propose the use of a security policy that allows for various security options commensurate with UPL, in conjunction with a context-aware security policy agent that notifies the service provider to activate new security appropriate to a change in UPL.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".