IP traceback through (authenticated) deterministic flow marking: an empirical evaluation
Bibliographic record
Abstract
In this paper, we present a novel approach to IP traceback - deterministic flow marking (DFM). We evaluate this novel approach against two well-known IP traceback schemes. These are the probabilistic packet marking (PPM) and the deterministic packet marking (DPM) techniques. In order to do so, we analyzed these techniques in detail in terms of their performances and feasibilities on five Internet traces. These traces consist of Darpa 1999 traffic traces, CAIDA October 2012 traffic traces, MAWI December 2012 traffic traces, and Dal2010 traffic traces. We have employed 16 performance metrics to evaluate their performances. The empirical results show that the novel DFM technique can reduce the number of marked packets by 91% compared to the DPM, while achieving the same or better performance in terms of its ability to trace back the attack. Additionally, DFM provides an optional authentication so that a compromised router cannot forge markings of other uncompromised routers. Unlike PPM and DPM that trace the attack up to the ingress interface of the edge router close to the attacker, DFM allows the victim to trace the origin of incorrect or spoofed source addresses up to the attacker node, even if the attack has been originated from a network behind a network address translation (NAT) server. Our results show that DFM can reach up to approximately 99% traceback rate with no false positives.
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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 | 0.021 | 0.141 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".