CAIR: Using Formal Languages to Study Routing, Leaking, and Interception in BGP

The Internet routing protocol BGP expresses topological reachability and policy-based decisions simultaneously in path vectors. A complete view on the Internet backbone routing is given by the collection of all valid routes, which is infeasible to obtain due to information hiding of BGP, the lack of omnipresent collection points, and data complexity. Commonly, graph-based data models are used to represent the Internet topology from a given set of BGP routing tables but fall short of explaining policy contexts. As a consequence, routing anomalies such as route leaks and interception attacks cannot be explained with graphs. In this paper, we use formal languages to represent the global routing system in a rigorous model. Our CAIR framework translates BGP announcements into a finite route language that allows for the incremental construction of minimal route automata. CAIR preserves route diversity, is highly efficient, and well-suited to monitor BGP path changes in real-time. We formally derive implementable search patterns for route leaks and interception attacks. In contrast to the state-of-the-art, we can detect these incidents. In practical experiments, we analyze public BGP data over the last seven years

Distributed Internet Paths Performance Analysis through Machine Learning

International audienceInternet path changes are frequently linked to path inflation and performance degradation; therefore, predicting their occurrence is highly relevant for performance monitoring and dynamic traffic engineering. In this paper we showcase DisNETPerf and NETPerfTrace, two different and complementary tools for distributed Internet paths performance analysis, using machine learning models

Improving the Routing Layer of Ad Hoc Networks Through Prediction Techniques

Cada dia és més evident el paper clau que juguen la informàtica/computació mòbil i les tecnologies sense fils a les nostres activitats diàries. Estar sempre connectat, en qualsevol moment i lloc, és actualment més una necessitat que un luxe. Els escenaris de computació ubics creats en base a aquests avenços tecnològics, permeten a les persones proporcionar i consumir informació compartida. En aquests escenaris, les xarxes que donen suport a aquestes comunicacions són típicament sense fils i ad hoc. Les característiques dinàmiques i canviants de les xarxes ad hoc, fan que el treball realitzat per la capa d'enrutament tingui un gran impacte en el rendiment d'aquestes xarxes. És molt important que la capa d'enrutament reaccioni ràpidament als canvis que es produeixen, i fins i tot s'avanci als que es produiran en un futur proper, mitjançant l'aplicació de tècniques de predicció. Aquesta tesi investiga si les tècniques de predicció poden millorar la capa d'enrutament de les xarxes ad hoc. Com a primer pas en aquesta direcció, explorem la potencialitat d'una estratègia de Predictor-Basat-en-Història (HBP) per predir la Informació de Control Topològic (TCI) generada pels protocols d'enrutament. Demostrem que hi ha una gran oportunitat per predir TCI, i aquesta predicció pot centrar-se en un petit subconjunt de missatges. En base a les nostres troballes, implementem el predictor OLSR-HBP i l'avaluem respecte al protocol Optimized Link State Routing (OLSR). OLSR-HBP aconsegueix disminucions importants de TCI (sobrecàrrega de senyalització), sense afectar el funcionament de la xarxa i necessita una quantitat de recursos petita i assequible. Finalment, en referència a l'impacte de la predicció en les dades d'enrutament tant de la informació de Qualitat d'Enllaç como de Ruta (o Extrem-a-Extrem), demostrem que l'Anàlisi de Sèries Temporals és un enfocament prometedor per predir amb precisió, tant la Qualitat d'Enllaç como la Qualitat d'Extrem a Extrem en Xarxes Comunitàries.Cada día es más evidente el papel clave que juegan la informática/computación móvil y las tecnologías inalámbricas en nuestras actividades diarias. Estar siempre conectado, en cualquier momento y lugar, es actualmente más una necesidad que un lujo. Los escenarios de computación ubicuos creados en base a estos avances tecnológicos, permiten a las personas proporcionar y consumir información compartida. En estos escenarios, las redes que dan soporte a estas comunicaciones son típicamente inalámbricas y ad hoc. Las características dinámicas y cambiantes de las redes ad hoc, hacen que el trabajo realizado por la capa de enrutamiento tenga un gran impacto en el rendimiento de estas redes. Es muy importante que la capa de enrutamiento reaccione rápidamente a los cambios que se producen, e incluso se adelante a los que sucederán en un futuro cercano, mediante la aplicación de técnicas de predicción. Esta tesis investiga si las técnicas de predicción pueden mejorar la capa de enrutamiento de las redes ad hoc. Como primer paso en esta dirección, exploramos la potencialidad de una estrategia de Predictor-Basado-en-Historia (HBP) para predecir la Información de Control Topológico (TCI) generada por los protocolos de enrutamiento. Demostramos que hay una gran oportunidad para predecir TCI, y esta predicción puede centrarse en un pequeño subconjunto de mensajes. En base a nuestros hallazgos, implementamos el predictor OLSR-HBP y lo evaluamos con respecto al protocolo Optimized Link State Routing (OLSR). OLSR-HBP consigue disminuciones importantes de TCI (sobrecarga de señalización), sin afectar al funcionamiento de la red, y necesita una cantidad de recursos pequeña y asequible. Finalmente, en referencia al impacto de la predicción en los datos de enrutamiento tanto de la información de Calidad de Enlace como de Ruta (o Extremo-a-Extremo), demostramos que el Análisis de Series Temporales es un enfoque prometedor para predecir con precisión, tanto la Calidad de Enlace como la Calidad de Extremo a Extremo en Redes Comunitarias.Everyday becomes more evident the key role that mobile computing and wireless technologies play in our daily activities. Being always connected, anytime, and anywhere is today more a necessity than a luxury. The ubiquitous computing scenarios created based on these technology advances allow people to provide and consume shared information. In these scenarios, the supporting communication networks are typically wireless and ad hoc. The dynamic and changing characteristics of the ad hoc networks, makes the work done by the routing layer to have a high impact on the performance of these networks. It is very important for the routing layer to quickly react to changes that happen, and even be advanced to what will happen in the near future, by applying prediction techniques. This thesis investigates whether prediction techniques can improve the routing layer of ad hoc networks. As a first step in this direction, in this thesis we explored the potentiality of a History-Based Predictor (HBP) strategy to predict the Topology Control Information (TCI) generated by routing protocols. We demonstrated that there is a high opportunity for predicting theTCI, and this prediction can be just focused on a small subset of messages. Based on our findings we implemented the OLSR-HBP predictor and evaluated it with regard to the Optimized Link State Routing (OLSR) protocol. OLSR History-Based Predictor (OLSR-HBP) achieved important decreases of TCI (signaling overhead), without disturbing the network operation, and requiring a small and affordable amount of resources. Finally, regarding the impact of Prediction on the routing data for both Link and Path (or End-to-End) Quality information, we demonstrated that Time-series analysis is a promising approach to accurately predict both Link and End-to-End Quality in Community Networks

Efficient Latent Semantic Extraction from Cross Domain Data with Declarative Language

With large amounts of data continuously generated by intelligence devices, efficient analysis of huge data collections to unearth valuable insights has become one of the most elusive challenges for both academia and industry. The key elements to establishing a scalable analyzing framework should involve (1) an intuitive interface to describe the desired outcome, (2) a well-crafted model that integrates all available information sources to derive the optimal outcome and (3) an efficient algorithm that performs the data integration and extraction within a reasonable amount of time. In this dissertation, we address these challenges by proposing (1) a cross-language interface for a succinct expression of recursive queries, (2) a domain specific neural network model that can incorporate information of multiple modalities, and (3) a sample efficient training method that can be used even for extremely-large output-class classifiers. Our contributions in this thesis are thus threefold: First, for the ubiquitous recursive queries in advanced data analytics, on top of BigDatalog and Apache Spark, we design a succinct and expressive analytics tool encapsulating the functionality and classical algorithms of Datalog, a quintessential logic programming language. We provide the Logical Library (LLib), a Spark MLlib-like high-level API supporting a wide range of recursive algorithms and the Logical DataFrame (LFrame), an extension to Spark DataFrame supporting both relational and logical operations. The LLib and LFrame enable smooth collaborations between logical applications and other Spark libraries and cross-language logical programming in Scala, Java, or Python. Second, we utilize variants of recurrent neural network (RNN) to incorporate some enlightening sequential information overlooked by the conventional works in two different domains including Spoken Language Understanding (SLU) and Internet Embedding (IE). In SLU, we address the problem caused by solely relying on the first best interpretation (hypothesis) of an audio command through a series of new architectures comprising bidirectional LSTM and pooling layers to jointly utilize the other hypotheses' texts or embedding vectors, which are neglected but with valuable information missed by the first best hypothesis. In IE, we propose the DIP, an extension of RNN, to build up the internet coordinate system with the IP address sequences, which are also unnoticed in conventional distance-based internet embedding algorithms but encode structural information of the network. Both DIP and the integration of all hypotheses bring significant performance improvements for the corresponding downstream tasks. Finally, we investigate the training algorithm for multi-class classifiers with a large output-class size, which is common in deep neural networks and typically implemented as a softmax final layer with one output neuron per each class. To avoid expensive computing the intractable normalizing constant of softmax for each training data point, we analyze the well-known negative sampling and improve it to the amplified negative sampling algorithm, which gains much higher performance with lower training cost

Predicting and tracking Internet path changes

This paper investigates to what extent it is possible to use trace-route-style probing for accurately tracking Internet path changes. When the number of paths is large, the usual traceroute based approach misses many path changes because it probes all paths equally. Based on empirical observations, we argue that monitors can optimize probing according to the likelihood of path changes. We design a simple predictor of path changes using a nearest-neighbor model. Although predicting path changes is not very accurate, we show that it can be used to improve probe targeting. Our path tracking method, called DTRACK, detects up to two times more path changes than traditional probing, with lower detection delay, as well as providing complete load-balancer information. Copyright 2011 ACM