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Record W3204157447 · doi:10.17762/de.vi.4924

An Ensemble Approach to Control Network Traffic on IoT

2021· article· en· W3204157447 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.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueDesign Engineering · 2021
Typearticle
Languageen
FieldComputer Science
TopicNetwork Security and Intrusion Detection
Canadian institutionsnot available
Fundersnot available
KeywordsComputer scienceNetwork packetInterruptInternet of ThingsComputer networkComputer securityServerTask (project management)Embedded systemMicrocontrollerEngineering

Abstract

fetched live from OpenAlex

Cyber security is most widely seen in many domains. From various domains these attacks plays the major role in damaging the servers and making websites unavailable. Many challenges are facing with the various cyber attacks.Internet of Things (IoT) is most widely used to define as a pervasive network of a (broad) range of connected smart nodes that offer diverse digital services, including the collection of environmental and user data. Detection of cyber attacks is difficult task and this may cause the loss of data packets and may interrupt the server. In this paper, the Enhancement is developed to handle the attack packets effectively. This is the window based application i,e based on the window size the data is processed. The Enhancement is called as an Amplified and Forward for Bi- Directional traffic from Attacks. This is very significant model to detect the several types of attacks that occur in IoT. Results show the performance of proposed system.

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)0.000
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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.819
Threshold uncertainty score0.551

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.015
GPT teacher head0.200
Teacher spread0.185 · 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