Bibliographic record
Abstract
This project addresses the need for an application level simulator to simulate Internet-wide phenomenon such as flash worms, botnets, Distributed Denial-of-Service attacks, etc. There are many network simulators intended for parallel and distributed simulation, but most are designed to simulate low level communication protocols such as TCP/IP. The desire to simulate rapidly spreading malware for research and teaching purposes lead us to explore the Spamulator, which was designed to simulate spam email on an Internet-wide scale. The Spamulator was developed by a team at the University of Calgary. It is a lightweight, application level simulator, which implements limited set of features of the Internet. In this project, the Spamulator is enhanced with the User Datagram Protocol (UDP) to simulate UDP worms. The modified version of the Spamulator is called the Wormulator. Wormulator tracks instantaneous network traffic, identifies and signals congestion throughout the network. The Wormulator is further enhanced with the use of POSIX threads instead of forking processes to create a distributed network of simulated servers. The resulting tool is called the “Enhanced Wormulator”. Finally, a random scanning UDP worm with behavior similar to the well known SQL Slammer worm is modeled to validate the results of our simulation. Results and data gathered from the simulation exhibit a qualitative resemblance to the realworld SQL Slammer worm. “Enhanced Wormulator”, which uses POSIX thread instead of forking a process, had a catalytic effect on the scalability factor of the simulation. The simulation was run on a network of 30,000 server nodes. Hence, we conclude that rapidly spreading malware can be effectively simulated using the Wormulator.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.063 | 0.028 |
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".