Jumps: Enhancing hop-count positioning in sensor networks using multiple coordinates

Positioning systems in self-organizing networks generally rely on measurements such as delay and received signal strength, which may be difficult to obtain and often require dedicated equipment. An alternative to such approaches is to use simple connectivity information, that is, the presence or absence of a link between any pair of nodes, and to extend it to hop-counts, in order to obtain an approximate coordinate system. Such an approximation is sufficient for a large number of applications, such as routing. In this paper, we propose Jumps, a positioning system for those self-organizing networks in which other types of (exact) positioning systems cannot be used or are deemed to be too costly. Jumps builds a multiple coordinate system based solely on nodes neighborhood knowledge. Jumps is interesting in the context of wireless sensor networks, as it neither requires additional embedded equipment nor relies on any nodes capabilities. While other approaches use only three hop-count measurements to infer the position of a node, Jumps uses an arbitrary number. We observe that an increase in the number of measurements leads to an improvement in the localization process, without requiring a high dense environment. We show through simulations that Jumps, when compared with existing approaches, reduces the number of nodes sharing the same coordinates, which paves the way for functions such as position-based routing

Optimización de P4P utilizando algoritmos genéticos para GoalBit

El uso de aplicaciones a través de Internet continua creciendo díia a díia. Una gran cantidad de estas aplicaciones son las denominadas peer-to-peer (P2P), y están consumiendo un importante porcentaje (entre 60 y 70 porciento) del ancho de banda de los proveedores de servicio a Internet (Internet Service Providers - ISPs). Este incremento en el consumo del ancho de banda global no sólo puede ocacionar problemas económicos a los ISPs, sino que puede traer problemas a las aplicaciones P2P que podrían verse bloqueadas por políticas de los ISPs. Para aplicaciones de transferencia de archivos (música, vídeos, juegos, etc.) puede que una limitante en el ancho de banda no genere grandes problemas, pero para aplicaciones de streaming en vivo de video o televisión, como por ejemplo GoalBit, el ancho de banda es crucial para el cumplimiento y calidad del servicio al usuario. Resulta entonces conveniente poder establecer un mecanismo por el cual las aplicaciones P2P y los ISPs puedan compartir informaciíon para lograr sus objetivos (evitar altos consumos de ancho de banda en los ISP y ofrecer un buen servicio y calidad a los usuarios de las aplicaciones P2P). Con este objetivo surge el proyecto P4P: Proactive Provider Participation for P2P, llevado adelante por la Universidad de Yale, Estados Unidos. En este artículo presentamos una aplicación del P4P para GoalBit, utilizando un Algortmo Genético Multiobjetivo para optimizar el uso de los recursos, minimizando el tráfico entre los ISPs al mismo tiempo que se maximiza el contenido recibido por los clientes (y por ende la calidad del streaming)

Analysis of the communication path attributes for IP geolocation

Cieľom tejto práce bolo preštudovať súčasné možnosti lokalizácie staníc v sieti Internet, hlavne aktívne metódy, ktoré sú založené na meraní oneskorenia. Popísať vznik oneskorenia a jeho jednotlivých častí. Ďalej vytvoriť aplikáciu, ktorá je schopná vzdialene merať oneskorenie medzi stanicami a prepočítavať oneskorenie na vzdialenosť. Aplikácia pomocou týchto vzdialeností zistí geografickú polohu zadanej stanice. Pri práci bola využitá experimentálna sieť PlanetLab.The aim of this thesis was to study current resources to find location of stations in the network Internet, mainly active methods that are based on delay measurements. Describe origin of the delay and its parts. Next create an application that is able remotely measure the delay between stations and convert this delay to distance. Aplication calculate geographic position of station on based this distances. For measurement was used experimental network PlanetLab.

Design and Implementation of Algorithms for Traffic Classification

Traffic analysis is the practice of using inherent characteristics of a network flow such as timings, sizes, and orderings of the packets to derive sensitive information about it. Traffic analysis techniques are used because of the extensive adoption of encryption and content-obfuscation mechanisms, making it impossible to infer any information about the flows by analyzing their content. In this thesis, we use traffic analysis to infer sensitive information for different objectives and different applications. Specifically, we investigate various applications: p2p cryptocurrencies, flow correlation, and messaging applications. Our goal is to tailor specific traffic analysis algorithms that best capture network traffic’s intrinsic characteristics in those applications for each of these applications. Also, the objective of traffic analysis is different for each of these applications. Specifically, in Bitcoin, our goal is to evaluate Bitcoin traffic’s resilience to blocking by powerful entities such as governments and ISPs. Bitcoin and similar cryptocurrencies play an important role in electronic commerce and other trust-based distributed systems because of their significant advantage over traditional currencies, including open access to global e-commerce. Therefore, it is essential to the consumers and the industry to have reliable access to their Bitcoin assets. We also examine stepping stone attacks for flow correlation. A stepping stone is a host that an attacker uses to relay her traffic to hide her identity. We introduce two fingerprinting systems, TagIt and FINN. TagIt embeds a secret fingerprint into the flows by moving the packets to specific time intervals. However, FINN utilizes DNNs to embed the fingerprint by changing the inter-packet delays (IPDs) in the flow. In messaging applications, we analyze the WhatsApp messaging service to determine if traffic leaks any sensitive information such as members’ identity in a particular conversation to the adversaries who watch their encrypted traffic. These messaging applications’ privacy is essential because these services provide an environment to dis- cuss politically sensitive subjects, making them a target to government surveillance and censorship in totalitarian countries. We take two technical approaches to design our traffic analysis techniques. The increasing use of DNN-based classifiers inspires our first direction: we train DNN classifiers to perform some specific traffic analysis task. Our second approach is to inspect and model the shape of traffic in the target application and design a statistical classifier for the expected shape of traffic. DNN- based methods are useful when the network is complex, and the traffic’s underlying noise is not linear. Also, these models do not need a meticulous analysis to extract the features. However, deep learning techniques need a vast amount of training data to work well. Therefore, they are not beneficial when there is insufficient data avail- able to train a generalized model. On the other hand, statistical methods have the advantage that they do not have training overhead

Detecting Middlebox Interference on Applications

PhDMiddleboxes are widely used in today’s Internet, especially for security and performance. Middleboxes classify, filter and shape traffic, therefore interfering with application behaviour and performing new network functions for end hosts. Recent studies have uncovered and studied middleboxes in different types of networks. In order to understand the middlebox interference on traffic flows and explore the involved ASes, our methodology relies on a client-server architecture, to be able to observe both directions of the middlebox interaction. Meanwhile, probing with increasing TTL values provides us chances to inspect behaviour of middleboxes hop by hop. Implementing our methodologies, we exploit a large-scale proxy infrastructure Luminati, to detect HTTP-interacting middleboxes across the Internet. We collect a large-scale dataset from vantage points distributed in nearly 10,000 ASes across 196 countries. Our results provide abundant evidence for middleboxes deployed across more than 1000 ASes. We observe various middlebox interference in both directions of traffic flows, and across a wide range networks, including mobile operators and data center networks

Комплексний підхід до задачі управління трафіком за наявності ІР-телефонії у корпоративній мережі

Мета даної роботи полягає у впровадженні комплексного підходу до задачі управління трафіком за наявності ІР-телефонії у корпоративній мережі. Об’єктом дослідження є процес передачі трафіка за наявності ІР-телефонії у корпоративній мережі. Предметом дослідження є технології управління трафіком за наявності ІР-телефонії у корпоративній мережі. У роботі використовувались наступні методи досліджень: метод аналізу, методи класифікації, метод натурного експерименту. Наукова новизна полягає у розробці комплексного підходу до задач управління трафіком в сучасних корпоративних мережах. Практична цінність полягає у тому, що застосування саме комплексного підходу до управління трафіком за наявності потокового трафіку (наприклад, ІР-телефонії), дозволить підвищити якість обслуговування в корпоративних мережах.The purpose of this work is to implement an integrated approach to the task of traffic management in the presence of IP telephony in the corporate network. The object of research is the process of traffic transfer/movement in the presence of IP-telephony in the corporate network. The subject of the research is: The technology of traffic management in the presence of IP telephony in the corporate network.. The following research methods were used in the work: method of analysis, method of classification, method of field experimentation. The scientific novelty consists of developing a comprehensive approach to the tasks of traffic management in modern corporate networks. The practical value lies in the fact that the use of an integrated approach for the management of traffic in the presence of stream traffic (eg, IP telephony), will improve the quality of service in corporate network

Towards Robust Traffic Engineering in IP Networks

To deliver a reliable communication service it is essential for the network operator to manage how traffic flows in the network. The paths taken by the traffic is controlled by the routing function. Traditional ways of tuning routing in IP networks are designed to be simple to manage and are not designed to adapt to the traffic situation in the network. This can lead to congestion in parts of the network while other parts of the network is far from fully utilized. In this thesis we explore issues related to optimization of the routing function to balance load in the network. We investigate methods for efficient derivation of the traffic situation using link count measurements. The advantage of using link counts is that they are easily obtained and yield a very limited amount of data. We evaluate and show that estimation based on link counts give the operator a fast and accurate description of the traffic demands. For the evaluation we have access to a unique data set of complete traffic demands from an operational IP backbone. Furthermore, we evaluate performance of search heuristics to set weights in link-state routing protocols. For the evaluation we have access to complete traffic data from a Tier-1 IP network. Our findings confirm previous studies who use partial traffic data or synthetic traffic data. We find that optimization using estimated traffic demands has little significance to the performance of the load balancing. Finally, we device an algorithm that finds a routing setting that is robust to shifts in traffic patterns due to changes in the interdomain routing. A set of worst case scenarios caused by the interdomain routing changes is identified and used to solve a robust routing problem. The evaluation indicates that performance of the robust routing is close to optimal for a wide variety of traffic scenarios. The main contribution of this thesis is that we demonstrate that it is possible to estimate the traffic matrix with good accuracy and to develop methods that optimize the routing settings to give strong and robust network performance. Only minor changes might be necessary in order to implement our algorithms in existing networks