54 research outputs found
AS relationships, customer cones, and validation
Business relationships between ASes in the Internet are typically confidential, yet knowledge of them is essential to understand many aspects of Internet structure, performance, dynamics, and evolution. We present a new algorithm to infer these relationships using BGP paths. Unlike previous approaches, our algorithm does not assume the presence (or seek to maximize the number) of valley-free paths, instead relying on three assumptions about the Internet's inter-domain structure: (1) an AS enters into a provider relationship to become globally reachable; and (2) there exists a peering clique of ASes at the top of the hierarchy, and (3) there is no cycle of p2c links. We assemble the largest source of validation data for AS-relationship inferences to date, validating 34.6% of our 126,082 c2p and p2p inferences to be 99.6% and 98.7% accurate, respectively. Using these inferred relationships, we evaluate three algorithms for inferring each AS's customer cone, defined as the set of ASes an AS can reach using customer links. We demonstrate the utility of our algorithms for studying the rise and fall of large transit providers over the last fifteen years, including recent claims about the flattening of the AS-level topology and the decreasing influence of "tier-1" ASes on the global Internet
Ricci Curvature of the Internet Topology
Analysis of Internet topologies has shown that the Internet topology has
negative curvature, measured by Gromov's "thin triangle condition", which is
tightly related to core congestion and route reliability. In this work we
analyze the discrete Ricci curvature of the Internet, defined by Ollivier, Lin,
etc. Ricci curvature measures whether local distances diverge or converge. It
is a more local measure which allows us to understand the distribution of
curvatures in the network. We show by various Internet data sets that the
distribution of Ricci cuvature is spread out, suggesting the network topology
to be non-homogenous. We also show that the Ricci curvature has interesting
connections to both local measures such as node degree and clustering
coefficient, global measures such as betweenness centrality and network
connectivity, as well as auxilary attributes such as geographical distances.
These observations add to the richness of geometric structures in complex
network theory.Comment: 9 pages, 16 figures. To be appear on INFOCOM 201
Router-level community structure of the Internet Autonomous Systems
The Internet is composed of routing devices connected between them and
organized into independent administrative entities: the Autonomous Systems. The
existence of different types of Autonomous Systems (like large connectivity
providers, Internet Service Providers or universities) together with
geographical and economical constraints, turns the Internet into a complex
modular and hierarchical network. This organization is reflected in many
properties of the Internet topology, like its high degree of clustering and its
robustness.
In this work, we study the modular structure of the Internet router-level
graph in order to assess to what extent the Autonomous Systems satisfy some of
the known notions of community structure. We show that the modular structure of
the Internet is much richer than what can be captured by the current community
detection methods, which are severely affected by resolution limits and by the
heterogeneity of the Autonomous Systems. Here we overcome this issue by using a
multiresolution detection algorithm combined with a small sample of nodes. We
also discuss recent work on community structure in the light of our results
Defending Tor from Network Adversaries: A Case Study of Network Path Prediction
The Tor anonymity network has been shown vulnerable to traffic analysis
attacks by autonomous systems and Internet exchanges, which can observe
different overlay hops belonging to the same circuit. We aim to determine
whether network path prediction techniques provide an accurate picture of the
threat from such adversaries, and whether they can be used to avoid this
threat. We perform a measurement study by running traceroutes from Tor relays
to destinations around the Internet. We use the data to evaluate the accuracy
of the autonomous systems and Internet exchanges that are predicted to appear
on the path using state-of-the-art path inference techniques; we also consider
the impact that prediction errors have on Tor security, and whether it is
possible to produce a useful overestimate that does not miss important threats.
Finally, we evaluate the possibility of using these predictions to actively
avoid AS and IX adversaries and the challenges this creates for the design of
Tor
Few Throats to Choke: On the Current Structure of the Internet
The original design of the Internet was as a resilient, distributed system, able to route around (and therefore recover from) massive disruption - up to and including nuclear war. However, network effects and business decisions (e.g. the pur- chase of GlobalCrossing by Level-3) have led to centralization of routing power. This is not merely an academic issue; it has practical implications, such as whether the citizens of a country may be subject to censorship by an “upstream” ISP in some other country, that controls its entire access to the Internet. In this paper, we examine the extent of routing centralization in the Internet; identify the major players who control the “Internet backbone”; and point out how many these are, in fact, under the jurisdiction of censorious countries. We also measure the collateral damage caused by censorship, particularly by the two largest Internet-using nations, China and India
A Machine Learning Approach to Edge Type Prediction in Internet AS Graphs
The Internet consists of a large number of interconnected autonomous systems (ASes). ASes engage in two types of business relationships to exchange traffic: provider-to-customer (p2c) relationship and peer-to-peer (p2p) relationship. Internet AS-level topology can be represented by AS graphs where nodes represent autonomous systems (ASes) and edges represent connectivity between ASes. While researchers have derived AS graphs using various data sources, inferring the types of edges (p2c or p2p) in AS graphs remains an open problem. In this paper we present a new machine learning approach to edge type inference in AS graphs. Our method uses the AdaBoost machine learning algorithm to train a model that predicts the edge types in a given AS graph using two node attributes - degree and minimum distance to a Tier-1 node. We train a model for a BGP graph and validate the model using ground truth AS relationships and CAIDA\u27s inferred AS relationship dataset. Our results show that the model achieves over 92% accuracy on a number of BGP graphs
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