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Record W1552464730

ScanSSH - Scanning the Internet for SSH Servers

2001· article· en· W1552464730 on OpenAlexfundno aff
Niels Provos, Peter Honeyman

Bibliographic record

VenueDeep Blue (University of Michigan) · 2001
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Malware Detection Techniques
Canadian institutionsnot available
FundersUniversity of Alberta
KeywordsServerComputer scienceLoginProtocol (science)The InternetAuthentication (law)Computer networkTransport Layer SecurityWeb serverCryptographic protocolCryptographyOperating systemComputer security
DOInot available

Abstract

fetched live from OpenAlex

SSH is a widely used application that provides secure remote login. It uses strong cryptography to provide authentication and condentiality. The IETF SecSH working group is developing SSH v2, an improved SSH protocol that xes cryptographic and design aws in the SSH v1 protocol. SSH v2 compatible server software is widespread. Recently discovered security aws make it critically important to nd vulnerable SSH servers and update them. In this paper, we describe a method to determine with good precision how many servers supporting the various protocol versions have been deployed on the net. We describe the design and implementation of ScanSSH, a scanner that probes SSH servers for their software version, and discuss the results of scanning the Internet and our local networks for several months. October 2, 2001 Center for Information Technology Integration University of Michigan 535 West William Street Ann Arbor, MI 48103-4943 . ScanSSH - Scanning the Internet for SSH Servers Niels Provos Peter Honeyman Center for Information Technology Integration University of Michigan 1

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)0.003
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: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.002
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.003

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 head0.011
GPT teacher head0.204
Teacher spread0.193 · 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

Citations30
Published2001
Admission routes1
Has abstractyes

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