On Evaluating IP Traceback Schemes: A Practical Perspective
Bibliographic record
Abstract
This paper presents an evaluation of two promising schemes for tracing cyber-attacks, the well-known Deterministic Packet Marking, DPM, and a novel marking scheme for IP traceback, Deterministic Flow Marking, DFM. First of all we explore the DPM in detail and then by investigating the DFM, we analyze the pros and cons of both approaches in depth in terms of practicality and feasibility, so that shortcomings of each scheme are highlighted. This evaluation is based on CAIDA Internet traces October 2012 dataset. The results show that using DFM may reduce as many as 90% of marked packets on average required for tracing attacks with no false positives, while it eliminates the spoofed marking embedded by the attacker as well as compromised routers in the attack path. Moreover, unlike DPM that traces 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), firewall, or a proxy server.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".