Groundwater Study: Toyota Motor Manufacturing, USA Georgetown, Kentucky

An eighteen month study of the Toyota Motor Manufacturing (TMM) plant site and the surrounding area was undertaken. The basic charge for this project was to characterize the groundwater that is potentially impacted by the TMM plant site. This included occurrence, flow direction, and, if possible, velocity. Because the area is karstified (has sinkholes, springs, caves, etc.) surface water and groundwater are intimately connected and, hence, surface water was frequently an important component of this work. Data from TMM construction plans and monitoring work done subsequent to construction were elicited from the various repositories within the TMM infrastructure. Aerial color photographs were acquired from various government agencies. Maps were constructed from the various data sources and data layers were combined to provide a complete picture of the plant site with respect to geology and groundwater. Fracture trace analysis and field reconnaissance was performed. Fifty-one sinkholes were found onsite, 182 in the entire study area. Several springs, both onsite and offsite, were discovered. Dye trace analysis was performed to determine connectivity and help to build a conceptual model of the subsurface flow system. Existing chemical analysis was complemented with chemical analysis done by the University of Kentucky

Machine Learning and Big Data Methodologies for Network Traffic Monitoring

Over the past 20 years, the Internet saw an exponential grown of traffic, users, services and applications. Currently, it is estimated that the Internet is used everyday by more than 3.6 billions users, who generate 20 TB of traffic per second. Such a huge amount of data challenge network managers and analysts to understand how the network is performing, how users are accessing resources, how to properly control and manage the infrastructure, and how to detect possible threats. Along with mathematical, statistical, and set theory methodologies machine learning and big data approaches have emerged to build systems that aim at automatically extracting information from the raw data that the network monitoring infrastructures offer. In this thesis I will address different network monitoring solutions, evaluating several methodologies and scenarios. I will show how following a common workflow, it is possible to exploit mathematical, statistical, set theory, and machine learning methodologies to extract meaningful information from the raw data. Particular attention will be given to machine learning and big data methodologies such as DBSCAN, and the Apache Spark big data framework. The results show that despite being able to take advantage of mathematical, statistical, and set theory tools to characterize a problem, machine learning methodologies are very useful to discover hidden information about the raw data. Using DBSCAN clustering algorithm, I will show how to use YouLighter, an unsupervised methodology to group caches serving YouTube traffic into edge-nodes, and latter by using the notion of Pattern Dissimilarity, how to identify changes in their usage over time. By using YouLighter over 10-month long races, I will pinpoint sudden changes in the YouTube edge-nodes usage, changes that also impair the end users’ Quality of Experience. I will also apply DBSCAN in the deployment of SeLINA, a self-tuning tool implemented in the Apache Spark big data framework to autonomously extract knowledge from network traffic measurements. By using SeLINA, I will show how to automatically detect the changes of the YouTube CDN previously highlighted by YouLighter. Along with these machine learning studies, I will show how to use mathematical and set theory methodologies to investigate the browsing habits of Internauts. By using a two weeks dataset, I will show how over this period, the Internauts continue discovering new websites. Moreover, I will show that by using only DNS information to build a profile, it is hard to build a reliable profiler. Instead, by exploiting mathematical and statistical tools, I will show how to characterize Anycast-enabled CDNs (A-CDNs). I will show that A-CDNs are widely used either for stateless and stateful services. That A-CDNs are quite popular, as, more than 50% of web users contact an A-CDN every day. And that, stateful services, can benefit of A-CDNs, since their paths are very stable over time, as demonstrated by the presence of only a few anomalies in their Round Trip Time. Finally, I will conclude by showing how I used BGPStream an open-source software framework for the analysis of both historical and real-time Border Gateway Protocol (BGP) measurement data. By using BGPStream in real-time mode I will show how I detected a Multiple Origin AS (MOAS) event, and how I studies the black-holing community propagation, showing the effect of this community in the network. Then, by using BGPStream in historical mode, and the Apache Spark big data framework over 16 years of data, I will show different results such as the continuous growth of IPv4 prefixes, and the growth of MOAS events over time. All these studies have the aim of showing how monitoring is a fundamental task in different scenarios. In particular, highlighting the importance of machine learning and of big data methodologies

Distributed reflection denial of service attack: A critical review

As the world becomes increasingly connected and the number of users grows exponentially and “things” go online, the prospect of cyberspace becoming a significant target for cybercriminals is a reality. Any host or device that is exposed on the internet is a prime target for cyberattacks. A denial-of-service (DoS) attack is accountable for the majority of these cyberattacks. Although various solutions have been proposed by researchers to mitigate this issue, cybercriminals always adapt their attack approach to circumvent countermeasures. One of the modified DoS attacks is known as distributed reflection denial-of-service attack (DRDoS). This type of attack is considered to be a more severe variant of the DoS attack and can be conducted in transmission control protocol (TCP) and user datagram protocol (UDP). However, this attack is not effective in the TCP protocol due to the three-way handshake approach that prevents this type of attack from passing through the network layer to the upper layers in the network stack. On the other hand, UDP is a connectionless protocol, so most of these DRDoS attacks pass through UDP. This study aims to examine and identify the differences between TCP-based and UDP-based DRDoS attacks

Multimedia delivery in the future internet

The term “Networked Media” implies that all kinds of media including text, image, 3D graphics, audio and video are produced, distributed, shared, managed and consumed on-line through various networks, like the Internet, Fiber, WiFi, WiMAX, GPRS, 3G and so on, in a convergent manner [1]. This white paper is the contribution of the Media Delivery Platform (MDP) cluster and aims to cover the Networked challenges of the Networked Media in the transition to the Future of the Internet. Internet has evolved and changed the way we work and live. End users of the Internet have been confronted with a bewildering range of media, services and applications and of technological innovations concerning media formats, wireless networks, terminal types and capabilities. And there is little evidence that the pace of this innovation is slowing. Today, over one billion of users access the Internet on regular basis, more than 100 million users have downloaded at least one (multi)media file and over 47 millions of them do so regularly, searching in more than 160 Exabytes1 of content. In the near future these numbers are expected to exponentially rise. It is expected that the Internet content will be increased by at least a factor of 6, rising to more than 990 Exabytes before 2012, fuelled mainly by the users themselves. Moreover, it is envisaged that in a near- to mid-term future, the Internet will provide the means to share and distribute (new) multimedia content and services with superior quality and striking flexibility, in a trusted and personalized way, improving citizens’ quality of life, working conditions, edutainment and safety. In this evolving environment, new transport protocols, new multimedia encoding schemes, cross-layer inthe network adaptation, machine-to-machine communication (including RFIDs), rich 3D content as well as community networks and the use of peer-to-peer (P2P) overlays are expected to generate new models of interaction and cooperation, and be able to support enhanced perceived quality-of-experience (PQoE) and innovative applications “on the move”, like virtual collaboration environments, personalised services/ media, virtual sport groups, on-line gaming, edutainment. In this context, the interaction with content combined with interactive/multimedia search capabilities across distributed repositories, opportunistic P2P networks and the dynamic adaptation to the characteristics of diverse mobile terminals are expected to contribute towards such a vision. Based on work that has taken place in a number of EC co-funded projects, in Framework Program 6 (FP6) and Framework Program 7 (FP7), a group of experts and technology visionaries have voluntarily contributed in this white paper aiming to describe the status, the state-of-the art, the challenges and the way ahead in the area of Content Aware media delivery platforms

Towards a software defined network based multi-domain architecture for the internet of things

The current communication networks are heterogeneous, with a diversity of devices and services that challenge traditional networks, making it difficult to meet quality of service (QoS) requirements. With the advent of software-defined networks (SDN), new tools have emerged to design more flexible networks. SDN offers centralized management for data streams in distributed sensor networks. Thus, the main goal of this dissertation is to investigate a solution that meets the QoS requirements of traffic originating on Internet of Things (IoT) devices. This traffic is transmitted to the Internet in a distributed system with multiple SDN controllers. To achieve the goal, we designed a multi-controller network topology, each managed by its controller. Communication between the domains is done via an SDN traffic domain with the Open Network Operating System (ONOS) controller SDN-IP application. We also emulated a network to test QoS through OpenvSwitch queues. The goal is to create traffic priorities in a network with traditional and simulated IoT devices. According to our tests, we have been able to ensure the SDN inter-domain communication and have proven that our proposal is reactive to a topology failure. In the QoS scenario we have shown that through the insertion of OpenFlow rules, we are able to prioritize traffic and provide guarantees of quality of service. This proves that our proposal is promising for use in scenarios with multiple administrative domains.As redes atuais de comunicação são heterogéneas, com uma diversidade de dispositivos e serviços, que desafiam as redes tradicionais, dificultando a satisfação dos requisitos de qualidade de serviço (QoS). Com o advento das Redes Definidas por Software (SDN), novas ferramentas surgiram para projetar redes mais flexíveis. O SDN oferece uma gestão centralizada para os fluxos de dados em redes distribuídas de sensores. Assim, o principal objetivo desta dissertação é de investigar uma solução que cumpra os requisitos de QoS do tráfego originado em dispositivos de Internet das coisas (IoT). Este tráfego é transmitido para a Internet, num sistema distribuído com múltiplos controladores SDN. Para atingir o objetivo, projetamos uma topologia de rede com múltiplos domínios, cada um gerido pelo seu controlador. A comunicação entre os domínios, é feita através dum domínio de trânsito SDN com a aplicação SDN-IP do controlador Sistema Operativo de Rede Aberta (ONOS). Emulamos também uma rede para testar a QoS através de filas de espera do OpenvSwitch. O objetivo é criar prioridades de tráfego numa rede com dispositivos tradicionais e de IoT simulados. De acordo com os testes realizados, conseguimos garantir a comunicação entre domínios SDN e comprovamos que a nossa proposta é reativa a uma falha na topologia. No cenário do QoS demostramos que, através da inserção de regras OpenFlow, conseguimos priorizar o tráfego e oferecer garantias de qualidade de serviço. Desta forma comprovamos que a nossa proposta é promissora para ser utilizada em cenários com múltiplos domínios administrativos

Design and implementation of InBlock, a distributed IP address registration system

The current mechanism to secure Border Gateway Protocol relies on the resource public key infrastructure (RPKI) for route origin authorization. The RPKI implements a hierarchical model that intrinsically makes lower layers in the hierarchy susceptible to errors and abuses from entities placed in higher layers. In this article, we present InBlock, a distributed autonomous organization that provides decentralized management of IP addresses based on blockchain, embedding an alternative trust model to the hierarchical one currently implemented by the RPKI. By leveraging on blockchain technology, InBlock requires consensus among the involved parties to change existent prefix allocation information. InBlock also fulfills the same objectives as the current IP address allocation system, i.e., uniqueness, fairness, conservation, aggregation, registration, and minimized overhead. InBlock is implemented as a set of blockchain smart contracts in Ethereum, performing all the functions needed for the management of a global pool of addresses without human intervention. Any entity may request an allocation of addresses to the InBlock registry by solely performing a (crypto) currency transfer to the InBlock. We describe our InBlock implementation and we perform several experiments to show that it enables fast address registering and incurs in very low management costs.Publicad

Virtual image sensors to track human activity in a smart house

With the advancement of computer technology, demand for more accurate and intelligent monitoring systems has also risen. The use of computer vision and video analysis range from industrial inspection to surveillance. Object detection and segmentation are the first and fundamental task in the analysis of dynamic scenes. Traditionally, this detection and segmentation are typically done through temporal differencing or statistical modelling methods. One of the most widely used background modeling and segmentation algorithms is the Mixture of Gaussians method developed by Stauffer and Grimson (1999). During the past decade many such algorithms have been developed ranging from parametric to non-parametric algorithms. Many of them utilise pixel intensities to model the background, but some use texture properties such as Local Binary Patterns. These algorithms function quite well under normal environmental conditions and each has its own set of advantages and short comings. However, there are two drawbacks in common. The first is that of the stationary object problem; when moving objects become stationary, they get merged into the background. The second problem is that of light changes; when rapid illumination changes occur in the environment, these background modelling algorithms produce large areas of false positives.These algorithms are capable of adapting to the change, however, the quality of the segmentation is very poor during the adaptation phase. In this thesis, a framework to suppress these false positives is introduced. Image properties such as edges and textures are utilised to reduce the amount of false positives during adaptation phase. The framework is built on the idea of sequential pattern recognition. In any background modelling algorithm, the importance of multiple image features as well as different spatial scales cannot be overlooked. Failure to focus attention on these two factors will result in difficulty to detect and reduce false alarms caused by rapid light change and other conditions. The use of edge features in false alarm suppression is also explored. Edges are somewhat more resistant to environmental changes in video scenes. The assumption here is that regardless of environmental changes, such as that of illumination change, the edges of the objects should remain the same. The edge based approach is tested on several videos containing rapid light changes and shows promising results. Texture is then used to analyse video images and remove false alarm regions. Texture gradient approach and Laws Texture Energy Measures are used to find and remove false positives. It is found that Laws Texture Energy Measure performs better than the gradient approach. The results of using edges, texture and different combination of the two in false positive suppression are also presented in this work. This false positive suppression framework is applied to a smart house senario that uses cameras to model ”virtual sensors” to detect interactions of occupants with devices. Results show the accuracy of virtual sensors compared with the ground truth is improved