Forwarding fault detection in wireless community networks

Wireless community networks (WCN) are specially vulnerable to routing forwarding failures because of their intrinsic characteristics: use of inexpensive hardware that can be easily accessed; managed in a decentralized way, sometimes by non-expert administrators, and open to everyone; making it prone to hardware failures, misconfigurations and malicious attacks. To increase routing robustness in WCN, we propose a detection mechanism to detect faulty routers, so that the problem can be tackled. Forwarding fault detection can be explained as a 4 steps process: first, there is the need of monitoring and summarizing the traffic observed; then, the traffic summaries are shared among peers, so that evaluation of a router's behavior can be done by analyzing all the relevant traffic summaries; finally, once the faulty nodes have been detected a response mechanism is triggered to solve the issue. The contributions of this thesis focus on the first three steps of this process, providing solutions adapted to Wireless Community Networks that can be deployed without the need of modifying its current network stack. First, we study and characterize the distribution of the error of sketches, a traffic summary function that is resilient to packet dropping, modification and creation and provides better estimations than sampling. We define a random process to describe the estimation for each sketch type, which allows us to provide tighter bounds on the sketch accuracy and choose the size of the sketch more accurately for a set of given requirements on the estimation accuracy. Second, we propose KDet, a traffic summary dissemination and detection protocol that, unlike previous solutions, is resilient to collusion and false accusation without the need of knowing a packet's path. Finally, we consider the case of nodes with unsynchronized clocks and we propose a traffic validation mechanism based on sketches that is capable of discerning between faulty and non-faulty nodes even when the traffic summaries are misaligned, i.e. they refer to slightly different intervals of time.Las redes comunitarias son especialmente vulnerables a errores en la retransmisión de paquetes de red, puesto que están formadas por equipos de gama baja, que pueden ser fácilmente accedidos por extraños; están gestionados de manera distribuida y no siempre por expertos, y además están abiertas a todo el mundo; con lo que de manera habitual presentan errores de hardware o configuración y son sensibles a ataques maliciosos. Para mejorar la robustez en el enrutamiento en estas redes, proponemos el uso de un mecanismo de detección de routers defectuosos, para así poder corregir el problema. La detección de fallos de enrutamiento se puede explicar como un proceso de 4 pasos: el primero es monitorizar el tráfico existente, manteniendo desde cada punto de observación un resumen sobre el tráfico observado; después, estos resumenes se comparten entre los diferentes nodos, para que podamos llevar a cabo el siguiente paso: la evaluación del comportamiento de cada nodo. Finalmente, una vez hemos detectado los nodos maliciosos o que fallan, debemos actuar con un mecanismo de respuesta que corrija el problema. Esta tesis se concentra en los tres primeros pasos, y proponemos una solución para cada uno de ellos que se adapta al contexto de las redes comunitarias, de tal manera que se puede desplegar en ellas sin la necesidad de modificar los sistemas y protocolos de red ya existentes. Respecto a los resumenes de tráfico, presentamos un estudio y caracterización de la distribución de error de los sketches, una estructura de datos que es capaz de resumir flujos de tráfico resistente a la pérdida, manipulación y creación de paquetes y que además tiene mejor resolución que el muestreo. Para cada tipo de sketch, definimos una función de distribución que caracteriza el error cometido, de esta manera somos capaces de determinar con más precisión el tamaño del sketch requerido bajo unos requisitos de falsos positivos y negativos. Después proponemos KDet, un protocolo de diseminación de resumenes de tráfico y detección de nodos erróneos que, a diferencia de protocolos propuestos anteriormente, no require conocer el camino de cada paquete y es resistente a la confabulación de nodos maliciosos. Por último, consideramos el caso de nodos con relojes desincronizados, y proponemos un mecanismo de detección basado en sketches, capaz de discernir entre los nodos erróneos y correctos, aún a pesar del desalineamiento de los sketches (es decir, a pesar del que estos se refieran a momentos de tiempo ligeramente diferentes)

Retroactive Packet Sampling for Traffic Receipts

Is it possible to design a packet-sampling algorithm that prevents the network node that performs the sampling from treating the sampled packets preferentially? We study this problem in the context of designing a "network transparency" system. In this system, networks emit receipts for a small sample of the packets they observe, and a monitor collects these receipts to estimate each network's loss and delay performance. Sampling is a good building block for this system, because it enables a solution that is flexible and combines low resource cost with quantifiable accuracy. The challenge is cheating resistance: when a network's performance is assessed based on the conditions experienced by a small traffic sample, the network has a strong incentive to treat the sampled packets better than the rest. We contribute a sampling algorithm that is provably robust to such prioritization attacks, enables network performance estimation with quantifiable accuracy, and requires minimal resources. We confirm our analysis using real traffic traces

Digital provenance - models, systems, and applications

Data provenance refers to the history of creation and manipulation of a data object and is being widely used in various application domains including scientific experiments, grid computing, file and storage system, streaming data etc. However, existing provenance systems operate at a single layer of abstraction (workflow/process/OS) at which they record and store provenance whereas the provenance captured from different layers provide the highest benefit when integrated through a unified provenance framework. To build such a framework, a comprehensive provenance model able to represent the provenance of data objects with various semantics and granularity is the first step. In this thesis, we propose a such a comprehensive provenance model and present an abstract schema of the model. ^ We further explore the secure provenance solutions for distributed systems, namely streaming data, wireless sensor networks (WSNs) and virtualized environments. We design a customizable file provenance system with an application to the provenance infrastructure for virtualized environments. The system supports automatic collection and management of file provenance metadata, characterized by our provenance model. Based on the proposed provenance framework, we devise a mechanism for detecting data exfiltration attack in a file system. We then move to the direction of secure provenance communication in streaming environment and propose two secure provenance schemes focusing on WSNs. The basic provenance scheme is extended in order to detect packet dropping adversaries on the data flow path over a period of time. We also consider the issue of attack recovery and present an extensive incident response and prevention system specifically designed for WSNs

Trustworthy Knowledge Planes For Federated Distributed Systems

In federated distributed systems, such as the Internet and the public cloud, the constituent systems can differ in their configuration and provisioning, resulting in significant impacts on the performance, robustness, and security of applications. Yet these systems lack support for distinguishing such characteristics, resulting in uninformed service selection and poor inter-operator coordination. This thesis presents the design and implementation of a trustworthy knowledge plane that can determine such characteristics about autonomous networks on the Internet. A knowledge plane collects the state of network devices and participants. Using this state, applications infer whether a network possesses some characteristic of interest. The knowledge plane uses attestation to attribute state descriptions to the principals that generated them, thereby making the results of inference more trustworthy. Trustworthy knowledge planes enable applications to establish stronger assumptions about their network operating environment, resulting in improved robustness and reduced deployment barriers. We have prototyped the knowledge plane and associated devices. Experience with deploying analyses over production networks demonstrate that knowledge planes impose low cost and can scale to support Internet-scale networks

Defense and traceback mechanisms in opportunistic wireless networks

 In this thesis, we have identified a novel attack in OppNets, a special type of packet dropping attack where the malicious node(s) drops one or more packets (not all the packets) and then injects new fake packets instead. We name this novel attack as the Catabolism attack and propose a novel attack detection and traceback approach against this attack referred to as the Anabolism defence. As part of the Anabolism defence approach we have proposed three techniques: time-based, Merkle tree based and Hash chain based techniques for attack detection and malicious node(s) traceback. We provide mathematical models that show our novel detection and traceback mechanisms to be very effective and detailed simulation results show our defence mechanisms to achieve a very high accuracy and detection rate