• Login
    View Item 
    •   Home
    • Research Publications
    • Engineering & Technology
    • Department of Electronics, Computing & Maths
    • View Item
    •   Home
    • Research Publications
    • Engineering & Technology
    • Department of Electronics, Computing & Maths
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Browse

    All of UDORACommunitiesTitleAuthorsIssue DateSubmit DateSubjectsThis CollectionTitleAuthorsIssue DateSubmit DateSubjects

    My Account

    LoginRegister

    About and further information

    AboutOpen Access WebpagesOpen Access PolicyTake Down Policy University Privacy NoticeUniversity NewsTools for ResearchersLibraryUDo

    Statistics

    Display statistics

    WiFi probes sniffing: an artificial intelligence based approach for MAC addresses de-randomization

    • CSV
    • RefMan
    • EndNote
    • BibTex
    • RefWorks
    Thumbnail
    Name:
    V2_Wi_Fi_Probes_sniffing__an_A ...
    Embargo:
    2022-09-30
    Size:
    357.0Kb
    Format:
    PDF
    Description:
    Accepted and revised article
    Download
    Authors
    Uras, Marco
    Cossu, Raimondo
    Ferrara, Enrico
    Bagdasar, Ovidiu
    Liotta, Antonio cc
    Atzori, Luigi
    Affiliation
    University of Derby
    University of Cagliari
    Free University of Bozen, Bolzano, Italy
    Issue Date
    2020-09-30
    
    Metadata
    Show full item record
    Abstract
    To improve city services, local administrators need to have a deep understanding of how the citizens explore the city, use the relevant services, interact and move. This is a challenging task, which has triggered extensive research in the last decade, with major solutions that rely on analysing traces of network traffic generated by citizens WiFi devices. One major approach relies on catching the probe requests sent by devices during WiFi active scanning, which allows for counting the number of people in a given area and to analyse the permanence and return times. This approach has been a solid solution until some manufacturer introduced the MAC address randomization process to improve the user’s privacy, even if in some circumstances this seems to deteriorate network performance as well as the user experience. In this work we present a novel techniques to tackle the limitations introduced by the randomization procedures and that allows for extracting data useful for smart cities development. The proposed algorithm extracts the most relevant information elements within probe requests and apply clustering algorithms (such as DBSCAN and OPTICS) to discover the exact number of devices which are generating probe requests. Experimental results showed encouraging results with an accuracy of 65.2% and 91.3% using the DBSCAN and the OPTICS algorithms, respectively.
    Citation
    Uras, M., Cossu, R., Ferrara, E., Bagdasar, O., Liotta, A. and Atzori, L., (2020). 'WiFi Probes sniffing: an Artificial Intelligence based approach for MAC addresses de-randomization' IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD). 14-16 September, Pisa, Italy, Italy. New York: IEEE, pp. 1-6.
    Publisher
    IEEE
    Journal
    2020 IEEE 25th International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD)
    URI
    http://hdl.handle.net/10545/625312
    DOI
    10.1109/camad50429.2020.9209257
    Additional Links
    https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9209257
    Type
    Meetings and Proceedings
    Language
    en
    ISBN
    9781728163390
    ae974a485f413a2113503eed53cd6c53
    10.1109/camad50429.2020.9209257
    Scopus Count
    Collections
    Department of Electronics, Computing & Maths

    entitlement

     
    DSpace software (copyright © 2002 - 2021)  DuraSpace
    Quick Guide | Contact Us
    Open Repository is a service operated by 
    Atmire NV
     

    Export search results

    The export option will allow you to export the current search results of the entered query to a file. Different formats are available for download. To export the items, click on the button corresponding with the preferred download format.

    By default, clicking on the export buttons will result in a download of the allowed maximum amount of items.

    To select a subset of the search results, click "Selective Export" button and make a selection of the items you want to export. The amount of items that can be exported at once is similarly restricted as the full export.

    After making a selection, click one of the export format buttons. The amount of items that will be exported is indicated in the bubble next to export format.