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Record W3212363874 · doi:10.1016/j.simpa.2021.100173

The Botnet Simulator: A simulation tool for understanding the interaction between botnets

2021· article· en· W3212363874 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueSoftware Impacts · 2021
Typearticle
Languageen
FieldComputer Science
TopicNetwork Security and Intrusion Detection
Canadian institutionsConcordia UniversityUniversité du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBotnetComputer scienceHuman–computer interactionSimulationWorld Wide WebThe Internet

Abstract

fetched live from OpenAlex

The Botnet Simulator is a tool that allows researchers to simulate the behavior of different bots that vie for control of the same pool of targets. Such simulations allow researchers to predict how differences in malware design such as different scanning strategies, infection methods or the presence of countermeasures will affect the spread of malware in a dynamic competitive environment. The main goal of the Botnet Simulator is to allow researchers to gain a better understanding of malware in order to develop effective countermeasures. Each botnet can be customized with any of several features that have been observed in real malware, thus allowing the creation of fine-grained and realistic simulations.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.963
Threshold uncertainty score0.862

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.055
GPT teacher head0.316
Teacher spread0.261 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it