Client Puzzles Based on Quasi Partial Collisions Against DoS Attacks in UMTS
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
The UMTS system and architecture are designed to accommodate Internet-like mobile services and specific services like mobile commerce to mobile users. They bring attacks from Internet and mobile users as well. Denial-of-service (DoS) attacks aim to frustrate a legitimate user's access to mobile services or bring down servers by depleting system resources. Many approaches are proposed to thwart these attacks. A client puzzle from the server, which forces the client to resolve it before communication, is one of these approaches. The server can adjust the difficulty levels of the puzzle for access control and against DoS attacks according to current resource consumption and communication scenario. Currently, many types of client puzzles have no fine-grained control over difficulties. In a client puzzle, the next higher difficulty level is often twice as hard as the current one. In this paper, we propose a method based on partial collisions in hash functions. Our approach provides fine-grained control over difficulties by introducing a quasi partial collision concept. The results obtained confirm the fine granularity and efficiency of our approach.
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.
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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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