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Record W2536432570 · doi:10.1109/tic-sth.2009.5444485

Evaluating security measures of a layered system

2009· article· en· W2536432570 on OpenAlexaff
Sanaz Hafezian Razavi, Olivia Das

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicDistributed systems and fault tolerance
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsComputer scienceVulnerability (computing)Layer (electronics)Host (biology)Enterprise information security architectureIntrusion detection systemDistributed computingComputer security

Abstract

fetched live from OpenAlex

Most distributed systems that we use in our daily lives have layered architecture since such architectures allow separation of processing between multiple processes in different layers thereby reducing the complexity of the system. Unauthorized control over such systems can have potentially serious consequences ranging from huge monetary loss to even loss of human life. Hence considerable research attention is being given towards building tools and techniques for quantitative modeling and evaluation of security properties. This paper proposes a high-level stochastic model to estimate security of a layered system. It discusses evaluation of availability and integrity as two major security properties of a 3 layered Architecture consisting of Client, Web-server, and Data base. Using Mobius software, this study models the change in vulnerability of a layer owing to an intrusion in another layer. Furthermore, it analyzes the impact on the security of the upper layers due to an intruded lower layer. While maintaining a system availability of 88.48%, this study indicates that increasing the system host attack rate in the Database layer from 10 to 100 will reduce system availability to 73%, while the same modification for Web-server layer will contribute to 60% availability.

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)0.001
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 score0.972
Threshold uncertainty score0.254

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 science0.0000.000
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 head0.064
GPT teacher head0.321
Teacher spread0.257 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations0
Published2009
Admission routes1
Has abstractyes

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