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Record W2766378937 · doi:10.1145/3139937.3139945

A Secure Event Logging System for Smart Homes

2017· article· en· W2766378937 on OpenAlex
Sepideh Avizheh, Tam Thanh Doan, Xi Liu, Reihaneh Safavi–Naini

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
TopicAdvanced Malware Detection Techniques
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsComputer scienceEvent (particle physics)Computer securityCloud computingComponent (thermodynamics)CryptographyHome automationLoggingComplex event processingEmbedded systemOperating system

Abstract

fetched live from OpenAlex

Smart homes include hundreds of devices that generate messages, and communicate with each other and the world outside the home, to provide a highly functional, optimized and personalized environment for residents. A secure and reliable event logging system is an essential component of smart homes with a wide range of applications such as fault detection, forensics and accounting. Existing smart home IoT frameworks are cloud-based and privacy of fine-grained log data is a real concern. In this paper we propose a host-based conceptual framework for storing and processing data in smart homes, analyze security requirements of such environments and design a forward secure event logging system that satisfies these environments. We give an overview of our implementation of a message (event) logging system for a typical home, and present efficiency evaluation of our cryptographic design.

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.764
Threshold uncertainty score0.315

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 science0.0010.000
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 head0.016
GPT teacher head0.289
Teacher spread0.274 · 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

Citations10
Published2017
Admission routes1
Has abstractyes

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