1 On the Impact of Route Monitor Selection Ying Zhang* Zheng Zhang # Z. Morley Mao* Y. Charlie Hu # Bruce M. Maggs ^ University of Michigan* Purdue University # Carnegie Mellon and Akamai Technologies ^

Internet route monitoring systems  Monitor the Internet routing system  Establish passive, default-free BGP sessions with many networks  Collect real-time BGP updates and periodic table snapshots  Discover dynamic changes (e.g., misconfigs, routing attacks)  Example public systems: RouteViews and RIPE 2 AS 7018 AS 3561 AS 174 Internet AS 701 AS 1239 Route monitor Prefix /24 “I can reach /24” via AE “I can reach /24” via DE

Limited coverage  Coverage and representativeness  Only monitor a subset of ASes in the Internet  Only monitor at most one router in each AS  Difficulties in obtaining full coverage  Scalability and privacy concerns 3 AS 7018 AS 3561 AS 174 Internet AS 701 AS 1239 Route monitor AS 237AS 105 “I can reach /24” via CDG “I can reach /24” via CFG

Limited visibility on IP Hijacking detection  The accuracy of detection depends on route monitor systems’ visibility  Example problems caused by limited visibility  IP prefix hijacking: ASG hijacks ASE’s prefix  Missed The route monitor system does not cover polluted ASes 4 AS 7018 AS 3561 AS 174 AS 701 AS 1239 Route monitor AS 237AS 105 Path[p] = ABE Path[p] = BE Path[p] = CE Path[p] = DE Path[p] = GDE Path[p] = FGDE Hijack: Path[p] = G Path[p] = AG Path[p] = BE Path[p] = CE Path[p] = DE Path[p] = G Path[p] = FG Path[p] = E Prefix p Prefix p’s origin AS is E Prefix p’s origin AS has changed to be G

Motivation  Many research studies rely on BGP data from public route monitors:  Network topology discovery, AS relationship inference, AS level path prediction, etc.  The limitation of coverage and representativeness of the monitors is critical to their results.  Obtaining full coverage is difficult in practice.  Understanding limitation can assist improved route monitor placement. 5

Outline  Motivation  Methodology  Discovery of static network properties  Discovery of dynamic network properties  Inference of network properties 6

Methodology  Data collection  Public BGP monitoring vantage points: RouteViews and RIPE  Private peering vantage points: 200 distinct ASes  Comparison across different combinations of vantage points  Monitor selection schemes  Random: select monitor nodes randomly  Degree based: select the node with largest degree  Greedy: select the node with largest unobserved links  Address block based: select the node originating largest IP addresses 7

Outline  Motivation  Methodology  Discovery of static network properties  Discovery of dynamic network properties  Inference of network properties 8

Static network properties  Network topology discovery  IP prefix to origin AS mappings  Identifying stub AS and its providers  Multi-homed ASes  Observed AS paths 9

Network topology discovery  The number of observed AS level links  Greedy based selection performs best 10

Multi-homed ASes discovery  Discover multi-homed ASes to understand edge network resilience  Greedy based scheme performs best: additional discovered links help discover multi-homed stub ASes 11

Outline  Motivation  Methodology  Discovery of static network properties  Discovery of dynamic network properties  Inference of network properties 12

Dynamic network properties  Routing instability monitoring  Number of routing updates observed  IP prefix hijacking detection  The visibility of inconsistent origin ASes across routing updates 13

Routing instability monitoring 14  Fraction of BGP routing events observed by the set of vantage points  Huge difference between random and other three: core networks are more likely to observe network instabilities

IP Prefix hijacking detection  Detected hijacking: as long as one vantage point can observe hijacked routes  Greedy based scheme performs slightly better 15 With 10 vantage points deployed, 0.35% of all possible attacker- victim pairs can evade detection

Outline  Motivation  Methodology  Discovery of static network properties  Discovery of dynamic network properties  Inference of network properties 16

Inference of network properties  AS relationship inference  Commonly used Gao’s degree-based relationship inference [Gao00]  AS-level path prediction  AS-relationship based profit-driven AS path inference [Mao05]  AS-relationship-independent path prediction [Muhlbauer06] 17

AS relationship inference and path prediction  Accuracy: comparing the predicted paths with the observed paths  More vantage points may not increase the accuracy 18

AS relationship inference and path prediction – further explanation  More vantage points may not increase the accuracy  It may be due to nature of the degree-based relationship inference  We study the changes of the top degree node per path  More vantage points do not consistently improve the estimation of the top degree nodes 19

Conclusion  Examined the route monitor placement impact on various applications  Evaluated four simple placement schemes  Demonstrated the limitation of studies relying on the existing monitoring system  Future work: develop a better placement technique. 20

Thank you! Questions? 21

AS relationship-independent path prediction  Recent proposed path prediction algorithm not relying on AS relationships  Matched percentage of unobserved does not increase with more monitors 22