A Framework for Enabling Security Services Collaboration Across Multiple Domains
Bibliographic record
Abstract
Collaboration among Security Service Functions (SSF) is expected to become as essential to SECaaS (SECurity as a Service) systems as elasticity is to IaaS (Infrastructure as a Service). The virtualization opens new era in network security as new security appliances can be created on demand in appropriate places in the network. At the same time, the increasing size and diversity of attacks make it necessary to come up with new approaches for more efficient and more resilient security mechanisms. In this paper, we propose a new framework leveraging SDN (Software Defined Networking) and SFC (Service Function Chaining) to enhance the collaboration among different SSFs to mitigate large scale attacks. We describe a framework that allows SSFs from different domains to negotiate and dynamically control the amount of resources allocated for collaboration, in what we call a "best-effort" collaboration mode. This SSF collaboration framework creates a distributed mitigation system for handling large scale attacks in a dynamic and scalable manner. The efficiency and feasibility of this framework is experimentally assessed, showing that our approach incurs low overhead, increases the amount of traffic treated by SSFs and reduces the dropped traffic due to the lack of resources from the security mechanisms.
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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 | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".