Detecting Malicious Switches for a Secure Software-defined Tactile Internet
Bibliographic record
Abstract
The rapid development of the Internet of Things has led to demand for high-speed data transformation. Serving this purpose is the Tactile Internet, which facilitates data transfer in extra-low latency. In particular, a Tactile Internet based on software-defined networking (SDN) has been broadly deployed because of the proven benefits of SDN in flexible and programmable network management. However, the vulnerabilities of SDN also threaten the security of the Tactile Internet. Specifically, an SDN controller relies on the network status (provided by the underlying switches) to make network decisions, e.g., calculating a routing path to deliver data in the Tactile Internet. Hence, the attackers can compromise the switches to jeopardize the SDN and further attack Tactile Internet systems. For example, an attacker can compromise switches to launch distributed denial-of-service attacks to overwhelm the SDN controller, which will disrupt all the applications in the Tactile Internet. In pursuit of a more secure Tactile Internet, the problem of abnormal SDN switches in the Tactile Internet is analyzed in this article, including the cause of abnormal switches and their influences on different network layers. Then we propose an approach that leverages the messages sent by all switches to identify abnormal switches, which adopts a linear structure to store historical messages at a relatively low cost. By mapping each flow message to the flow establishment model, our method can effectively identify malicious SDN switches in the Tactile Internet and thus enhance its security.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".