TULIP Trilateration Utility for Locating IP addresses

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TULIPTrilateration Utility for Locating IP
addresses

• Presented By
• Faran Javed
• BIT-5

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Project Committee
Advisor Prof. Dr. Arshad Ali
1
Co-Advisor Mr. Umar Kalim
2
Member Mr. Azhar Maqsood
3
Member Mr. Imran Daud
4
External Advisor Dr R. Les Cottrell
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Motivation

• Dynamic Geolocation solely based on delay
  measurements.
• Help identify hosts that have proxies
• To help determine from where to get a replicated
  service
• Useful for security to pin-point the location of
  a suspicious host
• Identify anomalies in the PingER database

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PingER

• PingER Ping end-to-End Reporting
• Name given to IEPM project
• Used to monitor end-to-end performance of
  Internet links

pingER historical graphs
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PingER Architecture
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Aim/Problem Statement

• To geolocate a specified target host (identified
  by domain name or public IP address) using only
  ping RTT delay measurements to the target from
  reference landmark hosts whose positions are well
  known.

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Related Work / Literature Survey
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Geo IP

• Mainly realize on end users input.
• Data acquired from various websites that offer
  end users membership.
• Further applies various techniques including
  triangulation.
• Conflicts are resolved manually.

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Literature Review 1/3

• CBG Constraint Based Geolocation bamba
• Works only within US
• Uses 90 reference landmarks
• Marks a possible region where the host may be
  located
• Currently not available
• NetGeo
• Stores location of each AS in a plain text file
• Database based approach. Prone to get outdated
• Needs updating every Saturday

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Literature Review 2/3

• Octant
• Efficient within US only
• Similar to CBG
• DNS LOC
• Rarely available
• Info provided by the network administrators
  themselves

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Literature Review 3/3

• Whois
• Gets outdated
• Database needs to be updated regularly

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Proposed Solution
Take Min RTT
Delay to Distance Conversion
Final (Lat , Lon)
Apply Trilateration
Iterative Correction
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Delay To Distance Conversion
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Adjusted Alpha values

• Methodology
• Plotted a scatter plot between distance in km
  minRTT (ms)
• The data set were the landmarks
• Drew the tightest upper bound on distances

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Adjusting Alpha
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Equation for the line representing the tightest
upper bound

• Two points on the line are
• i- origin ii- the point with highest value of
  ratio
• Dist / minRTT
• Line is represented by the equation
• Y mx b
• Y intercept is zero hence b 0
• M y2-y1 / x2-x1 y1 0 x1 0 origin
• M y2 / x2 y2Distance(km)x2minRTT(ms)
• Y mx Distance m minRTT
• Distance alpha minRTT
• M suggested alpha

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Iterrative Correction
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Iterative correction of the location

• minRTT propagation delay extra delay (due to
  extra circular routes)
• ?T measured ?t ?t0
• (Pseudo -distance)
• PD ?Tmeasured.a
• (Actual distance)
• D ?T.a
• PD (?T?T0).a
• PD D?T0. a . (1)

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Iterative correction

• D actual distance from the landmark.
• C speed of light
• a X(c) i.e. Speed of digital info in fiber
  optic cable
• X factor of c with which digital info travels
  in fiber optic cable.
• ?T actual propagation delay along the greater
  circle router/paths.
• ?T0 the extra delay causing overestimation.
• PD pseudo distance

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Graphically
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Landmarks

• H host
• L1 Landmark 1
• L2 landmark 2
• L3 landmark 3
• D1v (XL1-Xh) 2 (YL1-Yh) 2 .. (2)
• FROM (1) (2)
• PD1v (XL1-Xh) 2 (YL1-Yh) 2 a.?t0.. (A)
• Similarly for other 2 landmarks
• PD2v (XL2-Xh) 2 (YL2-Yh) 2 a.?t0.. (B)
• PD3v (XL3-Xh) 2 (YL3-Yh) 2 a.?t0..(C)

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Linearize the equation
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Contd

• Considering the simplified first part
• F(x) f(x0) f(x0) (x-x0)
• Put (x-x0?X)
• F(x) f(x0) f(x0) ?X (3)
• Hence to compute the original value of X an
  arbitrary value x0 is required, this is done by
  simple Trilateration.
• We know that
• Hx Xest?X. (D)
• HY Yest?Y.. (D)
• Also
• EstDiv (Lhi-Xest (Hy-Yest) 2 .. (4)

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Contd
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Contd
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• Solution from (4) is put in eq(D) to get new
  estimations.
• Hx, HY becomes the new estimated position.

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Design and Implementation
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System Architecture
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Results, Evaluations and Analysis
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Error Estimation Using Alpha
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• For each point calculate alpha distance/minRTT
• then calculate the median and Inter-quartile
  Range of the alphas.
• In the following case study we got 46.61median
  and IQR15.31.
• For this data median alpha 46.5km/ms and IQR
  15.6km/ms or IQR/Median 33 or -16.

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Alpha vs Distance
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Alpha Vs min RTT
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• Hence if we can calculate error in alpha we can
  calculate error in distance estimation and hence
  in the location estimate.

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Feasability for Teiring
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Tiering Approach

• The purpose of this study is to investigate the
  effectiveness of tiering for TULIP
• i.e we have a set of primary landmarks tier0
  which will narrow down the target location to
  being in a particular region and then a denser
  set of secondary tier1 landmarks in the
  discovered region that can be used to get more
  accurate results.

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Benefits

• The use of tiering should enable us to reduce the
  network traffic (number of landmarks pinging a
  target) while retaining the accuracy of using all
  landmarks.

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Alpha vs Distance (SLAC)
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Alpha vs MinRTT (SLAC)
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Accuracy Analysis
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TULIP Results
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Cumulative Distribution
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Conclusions

• TULIP offers coarse grain accuracy and can
  confirm location up to city level.
• Total of 14 differences ranging from 5,000 to
  13,000 were inaccuracies in PingER database.
• Further accuracy can be increase by increasing
  location data of landmark and a much careful
  landmark selection

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Applicability of TULIP

• TULIP is being used as the location estimation
  service for Phantom OS to assist in making VOs
  autonomously
• Being Used by SLAC to detect Anomalies in PingER
  database

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Problem Statement by Phantom OS

• PhantomOS resource discovery scheme is based on a
  two-tier based super peer based architecture. The
  lowest tier is a machine level granularity
  sub-grid, which consists of machines that have
  good network connectivity between them, analogous
  to a traditional cluster. Each sub-grid is
  represented by a super-peer, which is the most
  available machine within the vicinity of the
  sub-grid. At the top-most tier the granularity is
  in terms of sub-grids, and these are grouped into
  regions depending on geographical proximity of
  the super peers. The regions are represented by a
  region peer. A virtual organization (VO) in this
  system can be at any level it can consist of
  individual machines or be an aggregation of
  entire sub grids or of entire regions.
  Interactive applications will be handled at a
  machine-level VO, whereas large-scale grid
  applications will require aggregations of entire
  sub grids.
• With TULIP in PhantomOS, super peers will also
  provide the landmarks. New nodes will locate the
  nearest landmark and map to a subgrid which is
  spatially closest to them. Similarly Regions will
  be created by associating Subgrids to spatially
  close neighbouring subgrids. This information
  will also be provided by TULIP.

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Achievments and Challanges
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Challenges

• Increase accuracy in regions with poor network
  infrastructure
• Satellite links
• Circular routes
• Best Landmark Selection
• Security Considerations

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Achievement

• Stood First in All Asia Software Competition,
  Softec, Held at Fast Lahore.

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• Acknowledgment by SLAC daily newsletter

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Winner at NIIT Open House
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Future Directions
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Future Directions

• Centralized Reflector
• Complete Feasibility Analysis for Tiering
  approach
• Detailed visualization tools.
• Study on most suitable number of ping packets

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References

• 1 Constraint-Based Geolocation of Internet
  Hosts Bamba Gueye, Artur Ziviani, Mark Crovella
  and Serge Fdida,
• 2 Scale-free behavior of the Internet global
  performance R. Percacci1 and A. Vespignani2,
  Published online 7 May 2003 c EDP Sciences,
  Societa Italiana di Fisica, Springer-Verlag 2003
• 3 Geometric Exploration of the Landmark
  Selection Problem Liying Tang and Mark Crovella
  Department of Computer Science, Boston
  University, Boston, MA 02215 flitang,crovellag_at_cs.
  bu.edu
• 4 An Empirical Evaluation of Landmark Placement
  on Internet Coordinate Schemes Sridhar Srinivasan
  Ellen Zegura Networking and Telecommunications
  Group College of Computing Georgia Institute of
  Technology Atlanta, GA 30332, USA Email
  sridhar,ewz_at_cc.gatech.edu
• 5 A Network Positioning System for the
  Internet, T. S. Eugene Ng, Rice University, Hui
  Zhang, Carnegie Mellon University.
• 6 Towards IP Geolocation Using Delay and
  Topology Measurements Ethan Katz-Bassett John P.
  John Arvind Krishnamurthy David Wetherall Thomas
  Anderson Yatin Chawathe

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Demo

• Demo of current progress available at
• http//www.slac.stanford.edu/comp/net/wan-mon/tuli
  p
• Or
• http//maggie.niit.edu.pk/newwebsite/tulip
• Progress details also available at the Maggie
  wiki
• http//maggie2.niit.edu.pk/wiki

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Thank You !
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Appendix
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Previous value of alpha

• Speed of digital information in fiber optic cable
  2/3 c
• Since we have two side delay
• Alpha 2/3 c/2
• Put c 3 108 m/s
• We get alpha 100 km/ms

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Haversine Formula

• The haversine formula is an equation important in
  navigation, giving great-circle distances between
  two points on a sphere from their longitudes and
  latitudes.
• For two points on a sphere (of radius R) with
  latitudes f1 and f2, latitude separation ?f f1
  - f2, and longitude separation ??, where angles
  are in radians, the distance d between the two
  points (along a great circle of the sphere see
  spherical distance) is related to their locations
  by the formula