Evidence map›Paper›PMID 32413952›Full record

ArticleSensors (Basel, Switzerland)2020

Detection of Anomalous Behavior in Modern Smartphones Using Software Sensor-Based Data.

Victor Vlădăreanu, Valentin-Gabriel Voiculescu, Vlad-Alexandru Grosu, Luige Vlădăreanu, Ana-Maria Travediu, Hao Yan, Hongbo Wang, Laura Ruse

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Victor VlădăreanuInstitute of Solid Mechanics of the Romanian Academy, 010141 Bucharest, Romania.ORCID 0000-0002-2000-4792
Valentin-Gabriel VoiculescuFaculty of Electronics, Telecommunications and Information Technology, University Politehnica of Bucharest, 061071 Bucharest, Romania.ORCID 0000-0001-5947-505X
Vlad-Alexandru GrosuFaculty of Electronics, Telecommunications and Information Technology, University Politehnica of Bucharest, 061071 Bucharest, Romania.
Luige VlădăreanuInstitute of Solid Mechanics of the Romanian Academy, 010141 Bucharest, Romania.
Ana-Maria TravediuInstitute of Solid Mechanics of the Romanian Academy, 010141 Bucharest, Romania.
Hao YanParallel Robot and Mechatronic System Laboratory of Hebei Province, Yanshan University, Qinhuangdao 066004, China.
Hongbo WangParallel Robot and Mechatronic System Laboratory of Hebei Province, Yanshan University, Qinhuangdao 066004, China.
Laura RuseFaculty of Automatic Control and Computers, University Politehnica of Bucharest, 060042 Bucharest, Romania.

Funding

H2020 Marie Skłodowska-Curie Actions H2020-MSCA-RISE-2016-734875Unitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si Inovarii PN-III-P1-1.2-PCCDI2017-2017-0637
6 · The paper itself

Abstract

This paper describes the steps involved in obtaining a set of relevant data sources and the accompanying method using software-based sensors to detect anomalous behavior in modern smartphones based on machine-learning classifiers. Three classes of models are investigated for classification: logistic regressions, shallow neural nets, and support vector machines. The paper details the design, implementation, and comparative evaluation of all three classes. If necessary, the approach could be extended to other computing devices, if appropriate changes were made to the software infrastructure, based upon mandatory capabilities of the underlying hardware.

Indexed as

SmartphoneSoftwareLogistic ModelsNeural Networks, ComputerSupport Vector Machinemachine-learning classifiersmartphone securitysoftware sensor data

Identifiers

PMID32413952
PMCPMC7284384

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.