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Record W2139065819 · doi:10.1109/compsac.2009.54

A Framework for Cost Sensitive Assessment of Intrusion Response Selection

2009· article· en· W2139065819 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
TopicNetwork Security and Intrusion Detection
Canadian institutionsUniversity of New Brunswick
Fundersnot available
KeywordsIntrusion detection systemComputer scienceIntrusionPlug-inSelection (genetic algorithm)Process (computing)Response timeSet (abstract data type)Risk analysis (engineering)Reliability engineeringData miningEngineeringMachine learningOperating system

Abstract

fetched live from OpenAlex

In recent years, cost-sensitive intrusion response has gained significant interest, mainly due to its emphasis on the balance between potential damage incurred by the intrusion and cost of the response. However, one of the challenges in applying this approach is defining a consistent and adaptable measurement of these cost factors on the basis of system requirements and policy. In this paper,we present a host-based framework for the cost-sensitive assessment and selection of intrusion response. Specifically,we introduce a set of measurements that characterize the potential costs associated with the intrusion handling process, and propose an intrusion response evaluation method with respect to the risk of potential intrusion damage, the effectiveness of the response action and the response cost for a system. We provide an implementation of the proposed solution as an IDS-independent plugin tool and demonstrate its advantages on the several attack examples.

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)0.001
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.769
Threshold uncertainty score0.297

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 head0.018
GPT teacher head0.315
Teacher spread0.297 · 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

Quick stats

Citations43
Published2009
Admission routes1
Has abstractyes

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