Mitigating Security Problems in Virtualized Networks Through Resource Management
Bibliographic record
Abstract
Network Virtualization is the key to the current and future success of the Internet, and it has proven to be one of the core technologies in the evolution of the Internet.The virtualization of network resources offers many benefits.Resources are more efficiently utilized, and they can be deployed flexibly and elastically.It can lower the cost of ownership by moving from dedicated hardware to virtual resources.Another important benefit is the ease of management through standard abstractions.However, these benefits come at the cost of security.Network virtualization has increased the threat surface due to the virtualization of resources.Such as risk of isolation failure, privacy, and confidentiality of hosted services, side-channel attacks, and amplified impact of Denial-of-Service attacks.In this dissertation, we take a look at some of the security issues in the virtualized networks.We aim to utilize resource management to mitigate some of the security problems in the virtualized networks.In particular, we use Software-Defined Networking (SDN) and 5G mobile networks as a focus of our study to investigate and mitigate security issues.We identified that Distributed Denial-of-Service (DDoS) attacks pose a significant risk in SDN and emerging 5G mobile networks because both networks are virtualized, and the impact of DDoS is amplified.In a traditional network, there are usually very few stakeholders that are impacted by the DDoS.Whereas, in virtualized networks, many tenants share the same resources; therefore, many stakeholders can First, I would like to express my sincere gratitude to my supervisor, Prof. Ashraf Matrawy, for his continuous support, patience, motivation, and immense knowledge.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".