ANCHOR: logically-centralized security for Software-Defined Networks
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Abstract (arXiv)
While the centralization of SDN brought advantages such as a faster pace of innovation, it also disrupted some of the natural defenses of traditional architectures against different threats. The literature on SDN has mostly been concerned with the functional side, despite some specific works concerning non-functional properties like 'security' or 'dependability'. Though addressing the latter in an ad-hoc, piecemeal way, may work, it will most likely lead to efficiency and effectiveness problems. We claim that the enforcement of non-functional properties as a pillar of SDN robustness calls for a systemic approach. As a general concept, we propose ANCHOR, a subsystem architecture that promotes the logical centralization of non-functional properties. To show the effectiveness of the concept, we focus on 'security' in this paper: we identify the current security gaps in SDNs and we populate the architecture middleware with the appropriate security mechanisms, in a global and consistent manner. Essential security mechanisms provided by anchor include reliable entropy and resilient pseudo-random generators, and protocols for secure registration and association of SDN devices. We claim and justify in the paper that centralizing such mechanisms is key for their effectiveness, by allowing us to: define and enforce global policies for those properties; reduce the complexity of controllers and forwarding devices; ensure higher levels of robustness for critical services; foster interoperability of the non-functional property enforcement mechanisms; and promote the security and resilience of the architecture itself. We discuss design and implementation aspects, and we prove and evaluate our algorithms and mechanisms, including the formalisation of the main protocols and the verification of their core security properties using the Tamarin prover.
Key facts. Cited by 8 papers in the map (data through 14 Oct 2026). First posted on arXiv 9 Nov 2017.
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Citing papers per year: 2022: 1; 2023: 2; 2024: 0; 2025: 2; 2026: 3.
8 papers.
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- 2026Mind2Dialogue: Training Human-Aware Language Models by Simulating User Mental States14 Sep 2026
- 2026ALIGNBEAM : Inference-Time Alignment Transfer via Cross-Vocabulary Logit Mixing10 Jun 2026
- 2026TrajFlow: Nation-wide Pseudo GPS Trajectory Generation with Flow Matching Models16 Mar 2026
- 2025OpenFLAME: Federated Visual Positioning System to Enable Large-Scale Augmented Reality Applications4 Oct 2025
- 2025Zero-Shot Contextual Embeddings via Offline Synthetic Corpus Generation30 Jun 2025
- 2023Agglomerative Transformer for Human-Object Interaction Detection16 Aug 2023
- 2023Interpretable Regional Descriptors: Hyperbox-Based Local Explanations4 May 2023
- 2022Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation6 Jun 2022