Detecting and Preventing IP-spoofed Distributed DoS Attacks
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this paper, we explore mechanisms for defending against Distributed Denial of Service (DDoS) attacks, have become one of the major threats to the operation of the Internet today. We propose a novel scheme for detecting and preventing the most harmful and difficult to detect DDoS Attacks—those that use IP address spoofing to disguise the attack flow. Our scheme is based on a firewall that can distinguish the attack packets (containing spoofed source addresses) from the packets sent by legitimate users, and thus filters out most of the attack packets before they reach the victim. Unlike the other packet-marking based solutions, our scheme has a very low deployment cost; We estimate that an implementation of this scheme would require the cooperation of only about 20 % of the Internet routers in the marking process. The scheme allows the firewall system to configure itself based on the normal traffic of a Web server, so that the occurrence of an attack can be quickly and precisely detected. We have extensively tested our scheme by simulating DDoS attacks with up to several thousand attackers and the experimental results show that more than 90 % of attack packets can be effectively filtered-out without much affecting the flow of legitimate packets to the victim Web-server.
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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 it