MétaCan
Menu
Back to cohort
Record W2113254798 · doi:10.1109/iaw.2004.1437799

Searching covert channels by identifying malicious subjects in the time domain

2005· article· en· W2113254798 on OpenAlex
Changda Wang, Shiguang Ju

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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInternet Traffic Analysis and Secure E-voting
Canadian institutionsCarleton University
Fundersnot available
KeywordsCommunication sourceCovertCovert channelComputer scienceObject (grammar)Channel (broadcasting)Computer securityVulnerability (computing)Computer networkArtificial intelligenceCloud computing securityOperating systemSecurity information and event management

Abstract

fetched live from OpenAlex

Covert channel has three basic elements, i.e. sender subject, receiver subject and medium object. If the sender and receiver subjects change the medium object while they are communicating, though the sender and receiver are the origin, the security kernel will look them as different covert channels. By this method, covert communications between sender and receiver subjects can avoid being suppressed for covert channels of less than 100 bits per second are usually considered acceptable. Regardless the medium object's influence, a new search method was presented to overcome that vulnerability by identifying the malicious subjects in time domain.

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)0.000
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.974
Threshold uncertainty score0.477

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.012
GPT teacher head0.252
Teacher spread0.240 · 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