Packet Filtering Based on Source Router Marking and Hop-Count
Bibliographic record
Abstract
Denial of service (DoS) attacks impose an increasingly growing threat to the Internet These attacks result in wastage of scarce Internet resources and service disruptions. Existing packet filtering schemes are deployable at either source, intermediate or victim networks. In this paper, we propose a hybrid of the source and the victim networks-based packet filtering approach, source router marking and hop-count (SRHC), to detect and filter high-rate traffic flows and IP-spoofing attacks. Packets are marked at the source network based on their arrival rate threshold. At a victim network, the spoofed packets are marked based on the IP source arrival rate using their respective TTL value. Both source and victim networks collaborate to filter high-rate and IP-spoofing attacks. The ns-2 simulator is used to generate attack scenarios. Our simulation results show that the SRHC scheme effectively filters out high-rate and IP-spoofing attack packets, with minimal collateral damage.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".