DADI: Defending against distributed denial of service in information‐centric networking routing and caching
Bibliographic record
Abstract
Information‐centric networking (ICN) is a new communication paradigm for the upcoming next‐generation internet (NGI). ICN is an open environment that depends on in‐network caching and focuses on contents. These attributes make ICN architectures subject to different types of routing and caching attacks. An attacker publishes invalid contents or announces malicious routes and sends malicious requests for available and unavailable contents. These types of attacks can cause distributed denial of service (DDoS) and cache pollution in ICN architectures. In this paper,we propose a Defending solution Against DDoS in ICN routing and caching (DADI) that detects and prevents these DDoS attacks. This solution allows ICN routers to differentiate between legitimate and attack behaviors in the detection phase based on threshold values. In the prevention phase, ICN routers are able to take actions against these attacks. In our experiments, we measure satisfied requests for legitimate users and cache hit ratio for ICN routers, which are evaluated over different scenarios when there are 20%, 50%, and 80% attackers with respect to legitimate users. The experiments show that the proposed solution effectively mitigates routing‐ and caching‐related DDoS attacks in ICN and enhances ICN performance in the existence of DDoS attacks.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".