Blockchain-Driven Enhancement of SDN Security in IoT-Related Scenarios
Bibliographic record
Abstract
The major way of contact in today's landscape of interconnected global commercial activities occurs via cloud-based networks that transcend national, geographic, and jurisdictional barriers.Software Defined Networking (SDN), a developing networking architecture meant to ease policy enforcement and dynamic network reconfiguration, enables this seamless integration.Even with all the obvious advantages brought in by SDN, the problem of larger attack surface size compared to traditional networking infrastructures cannot be considered minor, particularly within the context of safety-critical applications.This problem gets even more exacerbated if SDN has to handle networking features relevant to the IoT.In particular, such deployments are more vulnerable to certain types of attacks.Added to that is the increasing need for inter-cloud communications in IoT applications, creating a nightmare from the security point of view.Furthermore, the number of connected nodes significantly complicates the situation and creates an overwhelming barrier toward monitoring all entities to prevent system degradation and service disruption.The paper aims to provide a general overview of frequent security challenges concerning SDN and IoT cloud integration, going deeper into the basic design concepts of the newly established paradigm called Blockchain, which could be considered a critical security aspect in each SDN or IoT application.Given the peculiar features of the paper, it proposes a Blockchain implementation solution to help nullify and minimize the various security issues which come about due to the convergence of SDN and IoT.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".