Graph Based Induction of unresponsive routers in Internet topologies

Kardes, Hakan; Güneş, Mehmet Hadi; Sarac, Kamil

doi:10.1016/j.comnet.2015.02.012

Cited by 6 publications

(1 citation statement)

References 35 publications

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“…ASM collects partial traces to every observed IP address of an AS from all border routers it could identify for the AS. After filtering trace anomalies such as loops and bounce-backs [44], ASM identifies subnetworks [45], IP aliases [46], and unresponsive routers [47] to infer the underlying link-level network of the AS. Even though ASM collects the most comprehensive snapshot of backbone AS, there may still be unmapped regions of the network.…”

Section: Sample Network Topologiesmentioning

confidence: 99%

Generation of 2-mode scale-free graphs for link-level internet topology modeling

Bakhshaliyev

Güneş

2020

PLoS ONE

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Comprehensive analysis that aims to understand the topology of real-world networks and the development of algorithms that replicate their characteristics has been significant research issues. Although the accuracy of newly developed network protocols or algorithms does not depend on the underlying topology, the performance generally depends on the topology. As a result, network practitioners have concentrated on generating representative synthetic topologies and utilize them to investigate the performance of their design in simulation or emulation environments. Network generators typically represent the Internet topology as a graph composed of point-to-point links. In this study, we discuss the implications of multi-access links on the synthetic network generation and modeling of the networks as bi-partite graphs to represent both subnetworks and routers. We then analyze the characteristics of sampled Internet topology data sets from backbone Autonomous Systems (AS) and observe that in addition to the commonly recognized power-law node degree distribution, the subnetwork size and the router interface distributions often exhibit power-law characteristics. We introduce a SubNetwork Generator (SubNetG) topology generation approach that incorporates the observed measurements to produce bipartite network topologies. In particular, generated topologies capture the 2-mode relation between the layer-2 (i.e., the subnetwork and interface distributions) and the layer-3 (i.e., the degree distribution) that is missing from the current network generators that produce 1-mode graphs. The SubNetG source code and experimental data is available at https://github.com/netml/sonet.

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Section: Sample Network Topologiesmentioning

confidence: 99%

Generation of 2-mode scale-free graphs for link-level internet topology modeling

Bakhshaliyev

Güneş

2020

PLoS ONE

Self Cite

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Network Topology Boundary Routing IP Identification for IP Geolocation

Yuan

Liu

et al. 2020

Lecture Notes in Computer Science

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Anonymous node location based on clustering

Luan

Xia

Xie

et al. 2021

J. Phys.: Conf. Ser.

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With the rapid development of network technology, the network’s scale is increasing and becoming more complex. It is imperative to understand the structure of the network for network management and network security. Therefore, the construction of the routing topology has become a top priority. Routing topology analysis is already a relatively mature technology, but anonymous nodes’ positioning has not received enough attention. Nowadays, the primary solutions to anonymous nodes are graph analysis and tomographic tree construction. Based on these two methods, this paper proposes a rough location of anonymous nodes based on the method of three-level clustering. They are the K-means clustering of data before anonymous identification, hierarchical clustering of feature values in anonymous identification, and central point calculation based on clustering ideas during anonymous positioning. Experiments have proved that performing the first-level K-means clustering before anonymous node identification can effectively improve anonymous positioning accuracy. And the three-layer clustering idea proposed in this paper can roughly get the location of anonymous nodes based on reducing the complexity of the original method.

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Graph Based Induction of unresponsive routers in Internet topologies

Cited by 6 publications

References 35 publications

Generation of 2-mode scale-free graphs for link-level internet topology modeling

Generation of 2-mode scale-free graphs for link-level internet topology modeling

Network Topology Boundary Routing IP Identification for IP Geolocation

Anonymous node location based on clustering

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