Skip to content
University of Maryland LibrariesDigital Repository at the University of Maryland
    • Login
    View Item 
    •   DRUM
    • Theses and Dissertations from UMD
    • UMD Theses and Dissertations
    • View Item
    •   DRUM
    • Theses and Dissertations from UMD
    • UMD Theses and Dissertations
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    IP Geolocation in Metropolitan Areas

    Thumbnail
    View/Open
    Singh_umd_0117E_12005.pdf (4.297Mb)
    No. of downloads: 6567

    Date
    2011
    Author
    Singh, Satinder Pal
    Advisor
    Shayman, Mark
    Metadata
    Show full item record
    Abstract
    In this thesis, we propose a robust methodology to geolocate a target IP Address in a metropolitan area. We model the problem as a Pattern Recognition problem and present algorithms that can extract patterns and match them for inferring the geographic location of target's IP Address. The first algorithm is a relatively non-invasive method called Pattern Based Geolocation (PBG) which models the distribution of Round Trip Times (RTTs) to a target and matches them to that of the nearby landmarks to deduce the target's location. PBG builds Probability Mass Functions (PMFs) to model the distribution of RTTs. For comparing PMFs, we propose a novel `Shifted Symmetrized Divergence' distance metric which is a modified form of Kullback-Leibler divergence. It is symmetric as well as invariant to shifts. PBG algorithm works in almost stealth mode and leaves almost undetectable signature in network traffic. The second algorithm, Perturbation Augmented PBG (PAPBG), gives a higher resolution in the location estimate using additional perturbation traffic. The goal of this algorithm is to induce a stronger signature of background traffic in the vicinity of the target, and then detect it in the RTT sequences collected. At the cost of being intrusive, this algorithm improves the resolution of PBG by approximately 20-40%. We evaluate the performance of PBG and PAPBG on real data collected from 20 machines distributed over 700 square miles large Washington-Baltimore metropolitan area. We compare the performance of the proposed algorithms with existing measurement based geolocation techniques. Our experiments show that PBG shows marked improvements over current techniques and can geolocate a target IP address to within 2-4 miles of its actual location. And by sending an additional traffic in the network PAPBG improves the resolution to within 1-3 miles.
    URI
    http://hdl.handle.net/1903/11505
    Collections
    • Electrical & Computer Engineering Theses and Dissertations
    • UMD Theses and Dissertations

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility
     

     

    Browse

    All of DRUMCommunities & CollectionsBy Issue DateAuthorsTitlesSubjectsThis CollectionBy Issue DateAuthorsTitlesSubjects

    My Account

    LoginRegister
    Pages
    About DRUMAbout Download Statistics

    DRUM is brought to you by the University of Maryland Libraries
    University of Maryland, College Park, MD 20742-7011 (301)314-1328.
    Please send us your comments.
    Web Accessibility