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Record W4376288712 · doi:10.22214/ijraset.2023.51956

Stingray Device for Cyber-Surveillance using a Software-Defined Radio as an IMSI Catcher

2023· article· en· W4376288712 on OpenAlexaboutno aff
R. Aishwarya, Sanjana Ramesh, V Balaji, Yash Sikhwal, Prof. Bhaskar Reddy

Bibliographic record

VenueInternational Journal for Research in Applied Science and Engineering Technology · 2023
Typearticle
Languageen
FieldComputer Science
TopicOpportunistic and Delay-Tolerant Networks
Canadian institutionsnot available
Fundersnot available
KeywordsComputer securityService (business)Computer scienceSoftwareHandoverLaw enforcementTelecommunicationsBusinessLaw

Abstract

fetched live from OpenAlex

Abstract: The Stingray or IMSI-catcher is a surveillance device for cellular phones that was initially developed by theHarris Corporation for military use. Nowadays, various local and state law enforcement agencies across countries such as Canada, the United States, and the United Kingdom use similar devices widely. The term Stingray has also become a general term for thistype of device. The IMSI catcher has two modes of operation- active and passive. In the active mode, the device pretends to be a cell tower, tricking all nearby mobile phones and cellular devices to connect to it. It can be mounted on vehicles, low flying airplanes and helicopters, UAVs, etc. It broadcasts signals that seem stronger than the cell tower, and thus, it forces each compatible cellular device to disconnect from its service provider (e.g., Jio, BSNL, etc.) and establish a newconnection with the device. Cellular communications protocols require mobile phones and cellular devices to connect to the strongest signal. We have used a Software Defined Radio (SDR) to replicate the Stingray device manufactured by the Harris Corporation. Although this device has a shorter range, it can still track the IMSI of all cellular devices around it. This project also demonstrates how fragile our privacy is concerning our devi

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)0.004
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.926
Threshold uncertainty score0.449

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0020.000
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 head0.101
GPT teacher head0.401
Teacher spread0.300 · 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 designSimulation or modeling
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

Citations0
Published2023
Admission routes1
Has abstractyes

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