IEEE/ACM Transactions on Networking (TON)
Delayed Internet routing convergence
IEEE/ACM Transactions on Networking (TON)
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Observation and analysis of BGP behavior under stress
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
Topology inference from BGP routing dynamics
Proceedings of the 2nd ACM SIGCOMM Workshop on Internet measurment
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Measuring the effects of internet path faults on reactive routing
SIGMETRICS '03 Proceedings of the 2003 ACM SIGMETRICS international conference on Measurement and modeling of computer systems
Using unlabeled data to improve text classification
Using unlabeled data to improve text classification
The Temporal and Topological Characteristics of BGP Path Changes
ICNP '03 Proceedings of the 11th IEEE International Conference on Network Protocols
Locating internet routing instabilities
Proceedings of the 2004 conference on Applications, technologies, architectures, and protocols for computer communications
Shrink: a tool for failure diagnosis in IP networks
Proceedings of the 2005 ACM SIGCOMM workshop on Mining network data
Diagnosis of TCP overlay connection failures using bayesian networks
Proceedings of the 2006 SIGCOMM workshop on Mining network data
Towards highly reliable enterprise network services via inference of multi-level dependencies
Proceedings of the 2007 conference on Applications, technologies, architectures, and protocols for computer communications
Semi-Supervised Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Information Theory - Part 2
| Hi-index | 0.00 |
Large-scale natural disasters cause external disturbances to networking infrastructure that lead to large-scale network-service disruption. To understand the impact of natural disasters to networks, it is important to localize and analyze network-service disruption after natural disasters occur. This work studies an inference of network-service disruption caused by the real natural disaster, Hurricane Katrina. We perform inference using large-scale Internet measurements and human inputs. We use clustering and feature extraction to reduce data dimensionality of sensory measurements and apply semi-supervised learning to jointly use sensory measurements and human inputs for inference. Our inference shows that after Katrina, approximately 25% of subnets were inferred as unreachable. We find that 62% of unreachable subnets were small subnets at the edges of networks, and 49% of these unreachabilities occurred after the landfall. The majority (73%) of unreachable subnets lasted longer than four weeks showing that Katrina caused extreme damage on networks and a slow recovery. Network-service disruption is inevitable after large-scale natural disasters occur. Thus, it is crucial to have effective inference techniques for more understanding of network responses and vulnerabilities to natural disasters.