A Trust Management Framework for Software Defined Networks-based Internet of Things
Bibliographic record
Abstract
The proliferation of smart objects and their connectivity have contributed to the realization of the Internet of Things (IoT) paradigm, offering a plethora of applications and services in many sectors of our lives. There are a number of communication architectures put forth to realize commercial IoT. Among other architectures, programmable networking solves most of the network management problems through Software Defined Networks (SDN). In SDN, the control plane is separated from the data plane and hence can be a perfect choice for IoT where things have to be managed from different perspectives such as communications, resource allocation, energy consumption, and so on. Addressing the problems of traditional networks through SDN indirectly advocates for its use in IoT environments. However, the "softwarization" of the network, i.e. SDN, poses new challenges to the network security. Among other security issues, lack of trust between the SDN controller and network management application (application that controls the network behavior) is one of the key security problems in SDN that may jeopardize IoT security. To fill the gaps, in this paper, we propose a trust establishment framework for SDN. The main idea is to establish direct trust between OpenFlow SDN controller and the applications. Our results show that by using the proposed trust management framework, financial losses could be considerably reduced for the networks.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".