Detecting and Preventing IP-spoofed Distributed DoS Attacks
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".