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Record W2575296097 · doi:10.1109/ccst.2016.7815721

Transitioning security software and hardware systems for use on unsecured or externally connected networks

2016· article· en· W2575296097 on OpenAlexaff
Ryan Kulchyski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Malware Detection Techniques
Canadian institutionsSenstar (Canada)
Fundersnot available
KeywordsComputer securityEncryptionComputer scienceNetwork securityAttack surfaceThe InternetNetwork Access ControlInternet securityIsolation (microbiology)Security serviceNetwork security policyCloud computing securityInformation securityWorld Wide WebOperating systemCloud computing

Abstract

fetched live from OpenAlex

The threat of online attacks has become a growing concern in technological environments across the globe. As is quite obvious, having a physical security system tampered with maliciously over an internet connection can be an enormous threat. Historically many security sensor companies have avoided this concern by leaving their security networks isolated from the external internet Presently we approach an age where operating an isolated network on a client site will become less and less appealing to the client With the added benefits of monitoring from their own network, use of existing infrastructure, remote connectivity options and the threat and or fear of an uncontrolled network on their site, many customers have a desire to have their security system on their own monitored, externally connected network An exploration is taken into the process of converting a product using its isolation for network security, into a product that can be dropped with confidence into an externally connected or potentially unsecure network environment. A range of concepts are covered at varying depths, including testing tools for getting started, encryption and reverse engineering, penetration testing, tools used for adding security and the difficulties and logistics of keeping patches up to date and virus definitions current.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.821
Threshold uncertainty score0.439

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 head0.020
GPT teacher head0.250
Teacher spread0.230 · 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
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

Citations0
Published2016
Admission routes1
Has abstractyes

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