IP Spoofing In and Out of the Public Cloud: From Policy to Practice
Bibliographic record
Abstract
In recent years, a trend that has been gaining particular popularity among cybercriminals is the use of public Cloud to orchestrate and launch distributed denial of service (DDoS) attacks. One of the suspected catalysts for this trend appears to be the increased tightening of regulations and controls against IP spoofing by world-wide Internet service providers (ISPs). Three main contributions of this paper are (1) For the first time in the research literature, we provide a comprehensive look at a number of possible attacks that involve the transmission of spoofed packets from or towards the virtual private servers hosted by a public Cloud provider. (2) We summarize the key findings of our research on the regulation of IP spoofing in the acceptable-use and term-of-service policies of 35 real-world Cloud providers. The findings reveal that in over 50% of cases, these policies make no explicit mention or prohibition of IP spoofing, thus failing to serve as a potential deterrent. (3) Finally, we describe the results of our experimental study on the actual practical feasibility of IP spoofing involving a select number of real-world Cloud providers. These results show that most of the tested public Cloud providers do a very good job of preventing (potential) hackers from using their virtual private servers to launch spoofed-IP campaigns on third-party targets. However, the same very own virtual private servers of these Cloud providers appear themselves vulnerable to a number of attacks that involve the use of spoofed IP packets and/or could be deployed as packet-reflectors in attacks on third party targets. We hope the paper serves as a call for awareness and action and motivates the public Cloud providers to deploy better techniques for detection and elimination of spoofed IP traffic.
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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 | 0.029 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.020 | 0.018 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.005 | 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".