Accuracy or delay? A game in detecting interest flooding attacks
Bibliographic record
Abstract
Abstract Due to the continuous recording of forwarding states, Information‐centric networking (ICN) introduces a new security threat named interest flooding attack. To mitigate this attack, most of the existing works focus on the detecting accuracy. However, we find another important factor that the detecting delay may result in long‐term memory occupation. In this letter, aiming to balance the detecting accuracy and delay, we propose an m‐list table‐based attack detecting (mTBAD) solution to minimize the detecting delay while guaranteeing the accuracy. Particularly, mTBAD maintains an m‐list table for malicious Interests entries by combining the disabling PIT exhaustion (DPE) and the negative acknowledgments (NACK). A lightweight monitor is equipped to issue m‐NACK packets to inform the attacked router and update its m‐list. Extensive simulations based on the GÉANT topology demonstrate that mTBAD reduces the detecting delay by 99.5% (from 280 to 1.2 milliseconds) compared with a state‐of‐the‐art mechanism, at the expense of a very slight loss regarding the false negative rate and the false positive rate. It proves that mTBAD can guarantee the detecting accuracy as well as to prevent long‐term memory occupation.
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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 | 0.006 | 0.035 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".