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Structural Results for Combined Continuous User Authentication and Intrusion Detection in High Security Mobile Ad-Hoc Networks

2011· article· en· W2131980718 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

VenueIEEE Transactions on Wireless Communications · 2011
Typearticle
Languageen
FieldComputer Science
TopicMobile Ad Hoc Networks
Canadian institutionsDefence Research and Development CanadaCarleton University
Fundersnot available
KeywordsComputer scienceMobile ad hoc networkIntrusion detection systemAuthentication (law)Partially observable Markov decision processWireless ad hoc networkComputer networkMarkov decision processMarkov processScheme (mathematics)Distributed computingMarkov chainComputer securityWirelessMarkov modelMachine learningNetwork packet

Abstract

fetched live from OpenAlex

Continuous user authentication is an important prevention-based approach to protect high security mobile ad-hoc networks (MANETs). On the other hand, intrusion detection systems (IDSs) are also important in MANETs to effectively identify malicious activities. Considering these two approaches jointly is effective in optimal security design taking into account system security requirements and resource constraints in MANETs. To obtain the optimal scheme of combining continuous user authentication and IDSs in a distributed manner, we formulate the problem as a partially observable Markov decision process (POMDP) multi-armed bandit problem. We present a structural results method to solve the problem for a large network with a variety of nodes. The policies derived from structural results are easy to implement in practical MANETs. Simulation results are presented to show the effectiveness and the performance of the proposed scheme.

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 categoriesMeta-epidemiology (narrow)
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 score0.972
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
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 head0.019
GPT teacher head0.244
Teacher spread0.226 · 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