TNGuard: Securing IoT Oriented Tenant Networks Based on SDN
Bibliographic record
Abstract
In the paradigm of infrastructure-as-a-service cloud computing involving an Internet of Things network, customers outsource their infrastructure to the cloud. An outsourced infrastructure is a virtual infrastructure that mimics the physical infrastructure of the precloud era; it is therefore referred to as a tenant network (TN) in this paper. This practice draws upon the notion of TN abstraction, which specifies how TNs should be managed. However, current virtual software-defined network (SDN) technology uses an SDN hypervisor to attain TNs, where the cloud administrator is given much-more-than-necessary privileges; thus, not only could violation of the security principle of least privilege occur, but the threat of a malicious or innocent-but-compromised administrator may be present. Motivated by this need, we propose the specification of TN abstraction, including its functions and security requirements. Then, we present a platform-independent concretization of this abstraction called TNGuard, which is an SDN-based architecture that protects the TNs while removing unnecessary privileges from the cloud administrator. In order to show that TNGuard concretizes the TN abstraction, we present an instantiation of TNGuard on the Xen virtualization platform with the Ryu controller. Experimental results show that the resulting system is practical, incurring a small performance overhead.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".