Inter and Intra Domain DDoS Attack Mitigation for Software Defined Network Based on Hyperledger Fabric Blockchain Technology
Bibliographic record
Abstract
The widespread adoption of Internet of Things devices has led to a significant rise in security concerns.Attackers can exploit the vulnerability of centralized control in softwaredefined networks (SDN) through distributed denial of service (DDoS) attacks on these networks.The concentration of control within a network introduces novel vulnerabilities and potential avenues for attacks.The present strategies employed for mitigating DDoS attacks face challenges arising from their constrained adaptability, inadequate allocation of resources, and reduced flexibility.The developing technology of blockchain offers a robust solution for cost-effective, optimized, and adaptable mitigation of inter and intra-domain SDN against DDoS attacks.This work utilizes the Hyperledger Fabric platform, a permissioned blockchain, to examine the detection of DDoS attacks using the entropy approach.The IP addresses of the victims are compiled into a blacklist, which is subsequently disseminated as transactions to generate a ledger of the blockchain over the network.Employing this method makes it unnecessary to obstruct the victim's ports.Two scenarios, namely, single and linear, have been employed to represent intradomain topology and one scenario for interdomain in the context of multicontroller environments.The experiment investigates the effects of two attack types, single attack and multi-attacker, across three different circumstances.The findings indicate that the duration of mitigation was decreased, demonstrating the efficacy of enhancing the overall network security with increased flexibility.This approach has promise for countering DDoS attacks.This work advances by using a permissioned network with an SDN to mitigate DDOS attacks and using drop packets rather than block ports.Using HLF makes setting various configurations possible, and this act can enhance performance.Results show that mitigation time in the three topologies (single, liner, and multi-controller) was 30, 21, and 48, respectively, at the victim side, while it takes 40, 43, and 60 at the controller side.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".