Proactive control of distributed denial of service attacks with source router preferential dropping
Bibliographic record
Abstract
Summary form only given. A distributed denial of service (DDoS) attack is an explicit attempt to interrupt an online service by generating a high volume of malicious traffic. These attacks consume all available network resources, thus rendering legitimate users unable to access the services. Most existing solutions propose to detect and drop attack packets at or near the destination network where the attack packets have already traversed the network and consumed considerable bandwidth. The aggregate traffic at the destination router may consist of hundreds of thousands of flows making it hard for the router to distinguish between legitimate and malicious packets. So, collateral damage is unavoidable. In this paper, we present a source router preferential dropping (SRPD) scheme to detect possible DDoS attacks and defeat them at their sources. SRPD monitors only high-rate outgoing flows at source networks and preferentially drops the packets belonging to these flows when it senses the existence of an attack. A simulation model is constructed and a number of simulation experiments have been conducted to evaluate the performance of the proposed scheme. Simulation results show that SRPD effectively controls DDoS attacks at their sources and reduces collateral damage to a minimum level.
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 | 0.002 |
| 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 | 0.000 | 0.000 |
| 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".