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Record W2396595559

Media access control address spoofing attacks against port security

2011· article· en· W2396595559 on OpenAlexaff
Andrew Buhr, Dale Lindskog, Pavol Zavarsky, Ron Ruhl

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicInternet Traffic Analysis and Secure E-voting
Canadian institutionsConcordia University of Edmonton
Fundersnot available
KeywordsSpoofing attackComputer securityComputer sciencePort (circuit theory)Network securityComputer networkFlexibility (engineering)Access controlNetwork Access ControlVendorCloud computing securityBusinessEngineering
DOInot available

Abstract

fetched live from OpenAlex

Abstract — In this paper we describe three separate Media Access Control (MAC) address spoofing attacks that, when deployed in specific yet common layer 2 network topologies, circumvent Cisco’s port security. We show first that, with full knowledge of the network, the vendor recommended implementation of port security is both ineffective at preventing all three of these attacks, and actually decreases the difficulty of performing two of them. Next, we re-examine the attacks under less ideal conditions and demonstrate that they are feasible. Finally, we describe mitigation strategies that reduce the likelihood of success, but we argue that the use of port security as a preventative measure is difficult and may require tradeoffs between security and performance, flexibility, administrative cost, and ease of use. Keywords-port security; spoofing attacks; mitigation stratigies I.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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 head0.035
GPT teacher head0.265
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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
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

Citations3
Published2011
Admission routes1
Has abstractyes

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