On the Impact of DDoS Attacks on Software-Defined Internet-of-Vehicles Control Plane
Bibliographic record
Abstract
To enhance the programmability and flexibility of network and service management, the Software-Defined Networking (SDN) paradigm is gaining growing attention by academia and industry. Motivated by its success in wired networks, researchers have recently started to embrace SDN towards developing next generation wireless networks such as Software-Defined Internet of Vehicles (SD-IoV). As the SD-IoV evolves, new security threats would emerge and demand attention. And since the core of the SD-IoV would be the control plane, it is highly vulnerable to Distributed Denial of Service (DDoS) Attacks. In this work, we investigate the impact of DDoS attacks on the controllers in a SD-IoV environment. Through experimental evaluations, we highlight the drastic effects DDoS attacks could have on a SD-IoV in terms of throughput and controller load. Our results could be a starting point to motivate further research in the area of SD-IoV security and would give deeper insights into the problems of DDoS attacks on SD-IoV.
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 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".