Secure Data Transportation With Software-Defined Networking and <i>k-n</i> Secret Sharing for High-Confidence IoT Services
Bibliographic record
Abstract
Internet of Things (IoT) has become a critical infrastructure in smart city services. Unlike traditional network nodes, most of the current IoT devices are constrained with limited capabilities. Moreover, frequent changes in the network status (e.g., nodes turns into the sleep mode to save battery) make it even more difficult to set up a stable, secure transmission among smart city IoT devices. On the one hand, these weaknesses make the IoT more vulnerable to attacks, such as data eavesdropping, which can monitor, tamper, and obtain the transporting data. On the other hand, the high-confidence smart city service strongly relies on the security of data transporting among the IoT devices, e.g., data being tempered would reduce the reliability of smart city services and data being monitored or stolen would infringe the privacy of smart city services. Toward high-confidence smart city IoT services, we proposed an approach to secure the data transportation among the smart city IoT devices, which combines a k-n secret-sharing mechanism and software-defined networking (SDN) technique to securely transport IoT data. Specifically, the data are transported by multiple routes calculated by the SDN controller adaptively. Data safety is guaranteed by the all-or-nothing feature of the k-n secret-sharing mechanism. Two SDN-based transmission strategies, which leverage the SDN's advantages on network management, and scheduling, are applied to overcome the challenges of the unstable network state in IoT. Extensive experiments conducted from many aspects show that the proposed approach can remarkably reduce the attack success rate with reasonable and acceptable 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| 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".