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Record W2164808023 · doi:10.1109/hicss.2011.201

Exploring Covert Channels

2011· article· en· W2164808023 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.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInternet Traffic Analysis and Secure E-voting
Canadian institutionsMcMaster University
FundersOffice of Naval Research
KeywordsCovert channelCovertSteganographyComputer securityComputer scienceInformation hidingConfidentialityChannel (broadcasting)Information securityPerceptionInternet privacyArtificial intelligenceComputer networkPsychologyEmbeddingSecurity information and event managementCloud computing security

Abstract

fetched live from OpenAlex

Covert channels pose a threat to system security for many reasons. One of the most significant security concerns surrounding the use of covert channels in computer and information systems involves confidentiality and the ability to leak confidential information from a high level security user to a low level one covertly. There are many differing views surrounding the ideas of covert channels and steganography with debates igniting over the existence of a relationship between the two concepts. This debate can be resolved with a model to provide a perception of covert channel communication to yield a better understanding of covert channels. In this paper, we propose a model to perceive covert channel communication. We use the proposed model to explore the relationship between covert channels, steganography and watermarking. The intent is to provide a better understanding of covert channel communication in an attempt to develop investigative support for confidentiality.

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)0.000
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 score0.991
Threshold uncertainty score0.384

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.139
GPT teacher head0.236
Teacher spread0.098 · 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