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Record W1996282515 · doi:10.1109/iccchina.2012.6356880

Towards hierarchical security framework for smartphones

2012· article· en· W1996282515 on OpenAlexaff
Hongwei Luo, Guili He, Xiaodong Lin, Xuemin Shen

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Malware Detection Techniques
Canadian institutionsOntario Tech UniversityUniversity of Waterloo
Fundersnot available
KeywordsComputer securityComputer sciencePopularitySecurity information and event managementCloud computing securitySecurity serviceSecurity through obscurityComputer security modelEnterprise information security architectureSecurity testingFocus (optics)Information securityCloud computingOperating system

Abstract

fetched live from OpenAlex

With powerful computing capability, plentiful functionality and advanced operating systems with flexible APIs, smartphones have become indispensable part of our daily lives. However, growing functionality, complexity and popularity of smartphones have also increased concerns about information security, and these concerns have been further exacerbated by rich third-party applications. In order to protect information security, significant research and standardizations efforts have been made in recent years. However, most of these activities focus on specific issues, which cannot mitigate negative effects as a whole. In this paper, we first introduce a common architecture of smartphones including main smartphone assets. Then we identify smartphone threats which are clustered into vulnerabilities and attacks. Based on the layered structure of smartphones, we propose a hierarchical security framework for smartphones including hardware security, operating system security, application security, user data security and communication security. Finally, we present the preliminary security solutions with regard to the security framework, and give future research direction.

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 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: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.380
Threshold uncertainty score0.356

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 science0.0000.000
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 head0.020
GPT teacher head0.309
Teacher spread0.290 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations7
Published2012
Admission routes1
Has abstractyes

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