109 research outputs found
Passport: Enabling Accurate Country-Level Router Geolocation using Inaccurate Sources
When does Internet traffic cross international borders? This question has
major geopolitical, legal and social implications and is surprisingly difficult
to answer. A critical stumbling block is a dearth of tools that accurately map
routers traversed by Internet traffic to the countries in which they are
located. This paper presents Passport: a new approach for efficient, accurate
country-level router geolocation and a system that implements it. Passport
provides location predictions with limited active measurements, using machine
learning to combine information from IP geolocation databases, router
hostnames, whois records, and ping measurements. We show that Passport
substantially outperforms existing techniques, and identify cases where paths
traverse countries with implications for security, privacy, and performance
Passport: enabling accurate country-level router geolocation using inaccurate sources
When does Internet traffic cross international borders? This question has major geopolitical, legal and social implications and is surprisingly difficult to answer. A critical stumbling block is a dearth of tools that accurately map routers traversed by Internet traffic to the countries in which they are located. This paper presents Passport: a new approach for efficient, accurate country-level router geolocation and a system that implements it. Passport provides location predictions with limited active measurements, using machine learning to combine information from IP geolocation databases, router hostnames, whois records, and ping measurements. We show that Passport substantially outperforms existing techniques, and identify cases where paths traverse countries with implications for security, privacy, and performance.First author draf
Systems for characterizing Internet routing
2018 Spring.Includes bibliographical references.Today the Internet plays a critical role in our lives; we rely on it for communication, business, and more recently, smart home operations. Users expect high performance and availability of the Internet. To meet such high demands, all Internet components including routing must operate at peak efficiency. However, events that hamper the routing system over the Internet are very common, causing millions of dollars of financial loss, traffic exposed to attacks, or even loss of national connectivity. Moreover, there is sparse real-time detection and reporting of such events for the public. A key challenge in addressing such issues is lack of methodology to study, evaluate and characterize Internet connectivity. While many networks operating autonomously have made the Internet robust, the complexity in understanding how users interconnect, interact and retrieve content has also increased. Characterizing how data is routed, measuring dependency on external networks, and fast outage detection has become very necessary using public measurement infrastructures and data sources. From a regulatory standpoint, there is an immediate need for systems to detect and report routing events where a content provider's routing policies may run afoul of state policies. In this dissertation, we design, build and evaluate systems that leverage existing infrastructure and report routing events in near-real time. In particular, we focus on geographic routing anomalies i.e., detours, routing failure i.e., outages, and measuring structural changes in routing policies
HLOC: Hints-Based Geolocation Leveraging Multiple Measurement Frameworks
Geographically locating an IP address is of interest for many purposes. There
are two major ways to obtain the location of an IP address: querying commercial
databases or conducting latency measurements. For structural Internet nodes,
such as routers, commercial databases are limited by low accuracy, while
current measurement-based approaches overwhelm users with setup overhead and
scalability issues. In this work we present our system HLOC, aiming to combine
the ease of database use with the accuracy of latency measurements. We evaluate
HLOC on a comprehensive router data set of 1.4M IPv4 and 183k IPv6 routers.
HLOC first extracts location hints from rDNS names, and then conducts
multi-tier latency measurements. Configuration complexity is minimized by using
publicly available large-scale measurement frameworks such as RIPE Atlas. Using
this measurement, we can confirm or disprove the location hints found in domain
names. We publicly release HLOC's ready-to-use source code, enabling
researchers to easily increase geolocation accuracy with minimum overhead.Comment: As published in TMA'17 conference:
http://tma.ifip.org/main-conference
Emergency rapid mapping with drones: models and solution approaches for offline and online mission planning
Die Verfügbarkeit von unbemannten Luftfahrzeugen (unmanned aerial vehicles oder UAVs) und die Fortschritte in der Entwicklung leichtgewichtiger Sensorik eröffnen neue Möglichkeiten für den Einsatz von Fernerkundungstechnologien zur Schnellerkundung in Großschadenslagen. Hier ermöglichen sie es beispielsweise nach Großbränden, Einsatzkräften in kurzer Zeit ein erstes Lagebild zur Verfügung zu stellen. Die begrenzte Flugdauer der UAVs wie auch der Bedarf der Einsatzkräfte nach einer schnellen Ersteinschätzung bedeuten jedoch, dass die betroffenen Gebiete nur stichprobenartig überprüft werden können. In Kombination mit Interpolationsverfahren ermöglichen diese Stichproben anschließend eine Abschätzung der Verteilung von Gefahrstoffen.
Die vorliegende Arbeit befasst sich mit dem Problem der Planung von UAV-Missionen, die den Informationsgewinn im Notfalleinsatz maximieren. Das Problem wird dabei sowohl in der Offline-Variante, die Missionen vor Abflug bestimmt, als auch in der Online-Variante, bei der die Pläne während des Fluges der UAVs aktualisiert werden, untersucht. Das übergreifende Ziel ist die Konzeption effizienter Modelle und Verfahren, die Informationen über die räumliche Korrelation im beobachteten Gebiet nutzen, um in zeitkritischen Situationen Lösungen von hoher Vorhersagegüte zu bestimmen.
In der Offline-Planung wird das generalized correlated team orienteering problem eingeführt und eine zweistufige Heuristik zur schnellen Bestimmung explorativer UAV-Missionen vorgeschlagen. In einer umfangreichen Studie wird die Leistungsfähigkeit und Konkurrenzfähigkeit der Heuristik hinsichtlich Rechenzeit und Lösungsqualität bestätigt. Anhand von in dieser Arbeit neu eingeführten Benchmarkinstanzen wird der höhere Informationsgewinn der vorgeschlagenen Modelle im Vergleich zu verwandten Konzepten aufgezeigt.
Im Bereich der Online-Planung wird die Kombination von lernenden Verfahren zur Modellierung der Schadstoffe mit Planungsverfahren, die dieses Wissen nutzen, um Missionen zu verbessern, untersucht. Hierzu wird eine breite Spanne von Lösungsverfahren aus unterschiedlichen Disziplinen klassifiziert und um neue effiziente Modellierungsvarianten für die Schnellerkundung ergänzt. Die Untersuchung im Rahmen einer ereignisdiskreten Simulation zeigt, dass vergleichsweise einfache Approximationen räumlicher Zusammenhänge in sehr kurzer Zeit Lösungen hoher Qualität ermöglichen. Darüber hinaus wird die höhere Robustheit genauerer, aber aufwändigerer Modelle und Lösungskonzepte demonstriert
Investigating Metropolitan Traffic Congestion in Albuquerque
Our project aimed to assist the New Mexico Department of Transportation in assessing Albuquerque congestion data. The team’s analysis will be used to support an application for a one-million-dollar federal grant that will be used to work on roadway infrastructure and communication between the agencies that focus on roadway safety. We researched incident hotspots on I-25 and I-40 and then compared pre- and post-crash surface road conditions in order to understand how highway incidents affect surface congestion for the NMDOT. The end result of our project included a written report summarizing our findings as well as visuals that were presented to representatives of the NMDOT, Albuquerque Traffic Management, MRCOG, and the NMDOT ITS Bureau
Artificial intelligence : A powerful paradigm for scientific research
Y Artificial intelligence (AI) coupled with promising machine learning (ML) techniques well known from computer science is broadly affecting many aspects of various fields including science and technology, industry, and even our day-to-day life. The ML techniques have been developed to analyze high-throughput data with a view to obtaining useful insights, categorizing, predicting, and making evidence-based decisions in novel ways, which will promote the growth of novel applications and fuel the sustainable booming of AI. This paper undertakes a comprehensive survey on the development and application of AI in different aspects of fundamental sciences, including information science, mathematics, medical science, materials science, geoscience, life science, physics, and chemistry. The challenges that each discipline of science meets, and the potentials of AI techniques to handle these challenges, are discussed in detail. Moreover, we shed light on new research trends entailing the integration of AI into each scientific discipline. The aim of this paper is to provide a broad research guideline on fundamental sciences with potential infusion of AI, to help motivate researchers to deeply understand the state-of-the-art applications of AI-based fundamental sciences, and thereby to help promote the continuous development of these fundamental sciences.Peer reviewe
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