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Record W2148415790 · doi:10.1109/ssiri.2011.24

ReLACK: A Reliable VoIP Steganography Approach

2011· article· en· W2148415790 on OpenAlexaff
Mohammad Hamdaqa, Ladan Tahvildari

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Steganography and Watermarking Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSteganographyVoice over IPComputer scienceSteganalysisNetwork packetComputer networkSteganography toolsReliability (semiconductor)Computer securityRedundancy (engineering)The InternetEmbeddingArtificial intelligence

Abstract

fetched live from OpenAlex

VoIP steganography is a real-time network steganography, which utilizes VoIP protocols and traffic as a covert channel to conceal secret messages. Recently, there has been a noticeable increase in the interest in VoIP steganography due to the volume of VoIP traffic generated, which proved to be economically feasible to utilize. This paper discusses VoIP steganography challenges, compares the existing mechanisms, and proposes a new VoIP steganography approach. Current VoIP steganography techniques lack mechanisms to provide reliability without weakening the steganography system. Accordingly, this paper modifies the (k, n) threshold secret sharing scheme, which is based on Lagrange's Interpolation, and then applies a two phase approach on the LACK steganography mechanism to provide reliability and fault tolerance and to increase steganalysis complexity. The cost of reliability is a loss in bandwidth, therefore, the proposed approach also provides mechanisms to maximize packets utilization to mitigate the effect of adding redundancy.

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 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: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.344
Threshold uncertainty score0.560

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.030
GPT teacher head0.222
Teacher spread0.192 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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".

Quick stats

Citations38
Published2011
Admission routes1
Has abstractyes

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