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Mao Y.,University of Texas at Austin | Mao Y.,AT and T Research | Wang F.,University of Texas at Austin | Wang F.,Meraki | And 3 more authors.
IEEE/ACM Transactions on Networking

Routing protocols for large wireless networks must address the challenges of reliable packet delivery at increasingly large scales and with highly limited resources. Attempts to reduce routing state can result in undesirable worst-case routing performance, as measured by stretch, which is the ratio of the hop count of the selected path to that of the optimal path. We present a new routing protocol, Small State and Small Stretch (S4), which jointly minimizes the state and stretch. S4 uses a combination of beacon distance-vector-based global routing state and scoped distance-vector-based local routing state to achieve a worst-case stretch of 3 using O(□N) routing state per node in an N-node network. Its average routing stretch is close to 1. S4 further incorporates local failure recovery to achieve resilience to dynamic topology changes. We use multiple simulation environments to assess performance claims at scale and use experiments in a 42-node wireless sensor network testbed to evaluate performance under realistic RF and failure dynamics. The results show that S4 achieves scalability, efficiency, and resilience in a wide range of scenarios. © 2006 IEEE. Source

Eriksson B.,University of Wisconsin - Madison | Barford P.,University of Wisconsin - Madison | Bowden R.,University of Adelaide | Duffield N.,AT and T Research | And 2 more authors.
Proceedings of the ACM SIGCOMM Internet Measurement Conference, IMC

The ability to detect unexpected events in large networks can be a significant benefit to daily network operations. A great deal of work has been done over the past decade to develop effective anomaly detection tools, but they remain virtually unused in live network operations due to an unacceptably high false alarm rate. In this paper, we seek to improve the ability to accurately detect unexpected network events through the use of BasisDetect, a flexible but precise modeling framework. Using a small dataset with labeled anomalies, the BasisDetect framework allows us to define large classes of anomalies and detect them in different types of network data, both from single sources and from multiple, potentially diverse sources. Network anomaly signal characteristics are learned via a novel basis pursuit based methodology. We demonstrate the feasibility of our Basis-Detect framework method and compare it to previous detection methods using a combination of synthetic and realworld data. In comparison with previous anomaly detection methods, our BasisDetect methodology results show a 50% reduction in the number of false alarms in a single node dataset, and over 65% reduction in false alarms for synthetic network-wide data. Copyright 2010 ACM. Source

Goldberg S.,Microsoft | Schapira M.,University of California at Berkeley | Hummon P.,AT and T Research
SIGCOMM'10 - Proceedings of the SIGCOMM 2010 Conference

In response to high-profile Internet outages, BGP security variants have been proposed to prevent the propagation of bogus routing information. To inform discussions of which variant should be deployed in the Internet, we quantify the ability of the main protocols (origin authentication, soBGP, S-BGP, and data-plane verification) to blunt traffic-attraction attacks; i.e., an attacker that deliberately attracts traffic to drop, tamper, or eavesdrop on packets. Intuition suggests that an attacker can maximize the traffic he attracts by widely announcing a short path that is not flagged as bogus by the secure protocol. Through simulations on an empirically-determined AS-level topology, we show that this strategy is surprisingly effective, even when the network uses an advanced security solution like S-BGP or data-plane verification. Worse yet, we show that these results underestimate the severity of attacks. We prove that finding the most damaging strategy is NP-hard, and show how counterintuitive strategies, like announcing longer paths, announcing to fewer neighbors, or triggering BGP loop-detection, can be used to attract even more traffic than the strategy above. These counterintuitive examples are not merely hypothetical; we searched the empirical AS topology to identify specific ASes that can launch them. Finally, we find that a clever export policy can often attract almost as much traffic as a bogus path announcement. Thus, our work implies that mechanisms that police export policies (e.g., defensive filtering) are crucial, even if S-BGP is fully deployed. © 2010 ACM. Source

Goyal M.,University of Wisconsin - Milwaukee | Soperi M.,University of Technology Malaysia | Baccelli E.,French Institute for Research in Computer Science and Automation | Choudhury G.,AT and T Research | And 3 more authors.
IEEE Communications Surveys and Tutorials

Open Shortest Path First (OSPF), a link state routing protocol, is a popular interior gateway protocol (IGP) in the Internet. Wide spread deployment and years of experience running the protocol have motivated continuous improvements in its operation as the nature and demands of the routing infrastructures have changed. Modern routing domains need to maintain a very high level of service availability. Hence, OSPF needs to achieve fast convergence to topology changes. Also, the ever-growing size of routing domains, and possible presence of wireless mobile adhoc network (MANET) components, requires highly scalable operation on part of OSPF to avoid routing instability. Recent years have seen significant efforts aimed at improving OSPF's convergence speed as well as scalability and extending OSPF to achieve seamless integration of mobile adhoc networks with conventional wired networks. In this paper, we present a comprehensive survey of these efforts. © 1998-2012 IEEE. Source

Bao X.,Samsung | Fan S.,Duke University | Varshavsky A.,AT and T Research | Li K.A.,AT and T Research | Choudhury R.R.,Duke University
UbiComp 2013 - Proceedings of the 2013 ACM International Joint Conference on Pervasive and Ubiquitous Computing

This paper describes a system for automatically rating content - mainly movies and videos - at multiple granularities. Our key observation is that the rich set of sensors available on to- day's smartphones and tablets could be used to capture a wide spectrum of user reactions while users are watching movies on these devices. Examples range from acoustic signatures of laughter to detect which scenes were funny, to the stillness of the tablet indicating intense drama. Moreover, unlike in most conventional systems, these ratings need not result in just one numeric score, but could be expanded to capture the user's experience. We combine these ideas into an Android based prototype called Pulse, and test it with 11 users each of whom watched 4 to 6 movies on Samsung tablets. Encouraging results show consistent correlation between the user's actual ratings and those generated by the system. With more rigorous testing and optimization, Pulse could be a candidate for real-world adoption. Copyright © 2013 ACM. Source

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